{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-09-27T12:58:33.700447Z",
     "start_time": "2024-09-27T12:58:33.695782Z"
    }
   },
   "source": [
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os\n",
    "\n",
    " \n"
   ],
   "outputs": [],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:09.833809Z",
     "start_time": "2024-09-27T12:58:57.130742Z"
    }
   },
   "cell_type": "code",
   "source": [
    "######################################数据的合并#########################################\n",
    "# 训练集\n",
    "train_LogInfo = pd.read_csv('./PPD_LogInfo_3_1_Training_Set.csv',encoding='gbk')\n",
    "train_Master = pd.read_csv('./PPD_Training_Master_GBK_3_1_Training_Set.csv',encoding='gbk')\n",
    "train_Userupdate = pd.read_csv('./PPD_Userupdate_Info_3_1_Training_Set.csv',encoding='gbk')\n",
    " \n",
    "#  测试集\n",
    "test_LogInfo = pd.read_csv('./PPD_LogInfo_2_Test_Set.csv',encoding='gbk')\n",
    "test_Master = pd.read_csv('./PPD_Master_GBK_2_Test_Set.csv',encoding='gb18030')\n",
    "test_Userupdate = pd.read_csv('./PPD_Userupdate_Info_2_Test_Set.csv',encoding='gbk')\n",
    " \n",
    "# 合并时用于标记哪些样本来自训练集和测试集\n",
    "train_Master['sample_status']='train'\n",
    "test_Master['sample_status']='test'\n",
    " \n",
    "# 训练集和测试集的合并(axis=0,增加行）\n",
    "df_Master = pd.concat([train_Master,test_Master],axis=0).reset_index(drop=True)\n",
    "df_LogInfo=pd.concat([train_LogInfo,test_LogInfo],axis=0).reset_index(drop=True)\n",
    "df_Userupdate=pd.concat([train_Userupdate,test_Userupdate],axis=0).reset_index(drop=True)\n",
    " \n",
    "df_Master.to_csv(\"./拍拍贷“魔镜杯”风控初赛数据/df_Master.csv\",encoding='gb18030',index=False)\n",
    "df_LogInfo.to_csv(\"./拍拍贷“魔镜杯”风控初赛数据/df_LogInfo.csv\",encoding='gb18030',index=False)\n",
    "df_Userupdate.to_csv(\"./拍拍贷“魔镜杯”风控初赛数据/df_Userupdate.csv\",encoding='gb18030',index=False)\n"
   ],
   "id": "d978c20425b89d0d",
   "outputs": [],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:12.117155Z",
     "start_time": "2024-09-27T12:59:09.835813Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#####################################数据的探索行分析#####################################\n",
    "# 导入合并后的数据\n",
    "df_Master = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/df_Master.csv',encoding='gb18030')\n",
    "df_LogInfo = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/df_LogInfo.csv',encoding='gb18030')\n",
    "df_Userupdate = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/df_Userupdate.csv',encoding='gb18030')\n",
    " \n",
    "# 定义显示形式\n",
    "pd.set_option(\"display.max_columns\",len(train_Master.columns)) \n",
    "df_Master.head(20)\n",
    "# 可以看到的是，数据主要分为：\n",
    "# 教育信息、第三方信息、社交网络信息、用户信息、网络博客信息、目标标签（target)和sample_status(自定义，用于区分数据来源于测试/训练集)\n",
    " \n",
    "# 察看训练集中好坏样本比例，1为坏样本\n",
    "df_Master.target.value_counts()\n",
    " \n",
    "# 每个个体都是独一的\n",
    "len(np.unique(df_Master.Idx))\n",
    "# df_Master.shape  # (49999, 229)"
   ],
   "id": "66d1da29a67ab87a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49999"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:38.371838Z",
     "start_time": "2024-09-27T12:59:36.863227Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#######################################（1）缺失值处理###################################\n",
    "# 原始中大量的缺失值用-1标识，我们将其替换成np.nan\n",
    "df_Master = df_Master.replace({-1:np.nan})\n",
    "df_Master.head(15)\n",
    " \n",
    "# 缺失值的可视化——白色越多，代表变量缺失越多\n",
    "# import missingno as msno\n",
    "# %matplotlib inline\n",
    "# msno.bar(df_Master)\n",
    " \n",
    "# 缺失占比超过80%的变量列表\n",
    "missing_columns=[]\n",
    "for column in df_Master.columns:\n",
    "    if sum(pd.isnull(df_Master[column]))/len(df_Master)>=0.8:\n",
    "        missing_columns.append(column)\n",
    "print(len(missing_columns))\n",
    "print(missing_columns)\n"
   ],
   "id": "bfde02f8c2729394",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n",
      "['WeblogInfo_1', 'WeblogInfo_3', 'ThirdParty_Info_Period7_1', 'ThirdParty_Info_Period7_2', 'ThirdParty_Info_Period7_3', 'ThirdParty_Info_Period7_4', 'ThirdParty_Info_Period7_5', 'ThirdParty_Info_Period7_6', 'ThirdParty_Info_Period7_7', 'ThirdParty_Info_Period7_8', 'ThirdParty_Info_Period7_9', 'ThirdParty_Info_Period7_10', 'ThirdParty_Info_Period7_11', 'ThirdParty_Info_Period7_12', 'ThirdParty_Info_Period7_13', 'ThirdParty_Info_Period7_14', 'ThirdParty_Info_Period7_15', 'ThirdParty_Info_Period7_16', 'ThirdParty_Info_Period7_17', 'SocialNetwork_3', 'SocialNetwork_4', 'SocialNetwork_5', 'SocialNetwork_6', 'SocialNetwork_7', 'SocialNetwork_11']\n"
     ]
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:49.866968Z",
     "start_time": "2024-09-27T12:59:38.373840Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 筛掉缺失大于80%的变量\n",
    "df_Master = df_Master.loc[:,list(~df_Master.columns.isin(missing_columns))]\n",
    "df_Master.shape\n",
    " \n",
    "# 再来看样本的特征缺失（行缺失）\n",
    "# 对于某个样本，特征缺失大于100\n",
    "missing_index=[]\n",
    "for i in np.arange(df_Master.shape[0]):\n",
    "    if list(df_Master.loc[i,:].isnull()).count(True)>100:\n",
    "        missing_index.append(i)\n",
    "print(missing_index)\n"
   ],
   "id": "9c488189fd5d48a6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7, 105, 963, 1036, 1630, 2187, 2464, 2606, 2660, 2675, 2707, 2769, 3093, 3145, 3326, 3387, 3643, 3865, 3910, 3924, 3991, 3993, 4096, 4112, 4145, 4164, 4243, 4521, 4674, 4688, 4703, 4890, 4900, 5001, 5019, 5340, 5358, 5381, 5417, 5668, 5933, 5939, 6148, 6331, 6385, 6418, 6710, 6754, 6759, 6805, 6845, 6880, 6885, 6902, 6928, 6958, 7042, 7184, 7201, 7249, 7300, 7404, 7424, 7452, 7477, 7510, 7730, 7799, 7832, 8026, 8059, 8135, 8639, 8696, 8705, 9033, 9131, 9181, 9395, 9412, 9423, 9602, 9974, 10109, 10119, 10150, 10493, 10543, 10627, 10709, 10742, 11300, 11367, 11413, 11536, 11562, 11901, 11953, 12205, 12527, 12630, 13001, 13074, 13266, 13488, 13551, 13712, 13744, 13955, 14406, 14455, 14943, 15123, 15251, 15336, 15381, 15785, 16254, 16267, 16340, 16639, 16765, 16826, 16932, 17950, 18732, 18910, 19072, 19130, 19339, 19597, 19715, 19868, 19950, 20182, 20436, 20647, 20842, 21857, 21978, 22042, 22380, 22427, 22609, 22666, 22898, 22924, 24526, 24643, 24846, 26066, 26131, 26143, 26326, 26329, 26498, 27241, 27463, 27482, 27644, 27771, 28248, 28401, 28506, 28708, 29718, 29757, 29886, 30686, 30812, 30957, 31189, 31359, 31661, 31766, 31847, 31864, 32130, 32464, 32493, 32515, 32589, 32638, 32772, 32858, 32995, 33012, 33054, 33144, 33410, 33439, 33460, 33474, 33547, 33675, 33684, 33734, 33750, 33851, 33909, 33944, 34064, 34087, 34114, 34390, 34492, 34606, 34720, 34821, 34901, 34924, 34990, 35174, 35504, 35550, 35623, 35726, 35755, 35834, 36016, 36127, 36131, 36163, 36330, 36534, 37388, 38020, 38031, 38109, 38210, 38276, 38406, 38666, 39147, 39204, 39474, 39674, 39682, 39743, 39783, 39848, 40000, 40060, 40112, 40155, 40157, 40685, 40758, 40790, 40841, 41002, 41055, 41768, 41786, 42197, 42666, 43156, 43496, 43514, 43549, 43701, 44031, 44123, 44279, 44299, 44549, 44671, 44740, 44891, 45374, 45417, 45948, 46320, 46838, 47326, 47569, 47572, 47649, 47734, 48035, 48191, 48192, 48473, 48498, 48674, 48857, 48863, 49142, 49725, 49787, 49790, 49860, 49866, 49897, 49899, 49904, 49970, 49991]\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.429260Z",
     "start_time": "2024-09-27T12:59:49.867970Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 删除特征缺失超过100的行\n",
    "df_Master = df_Master.drop(missing_index).reset_index(drop=True)\n",
    "df_Master.shape\n",
    " \n",
    "# 单变量占比分析\n",
    "print(\"原变量总数：\",'\\n',len(df_Master.columns))\n",
    "cols = [col for col in df_Master.columns if col not in ('target','sample_status')]\n",
    "print(\"排除目标标签和标记训练集和测试集来源的变量总数：\",'\\n',len(cols))\n",
    " \n",
    " \n",
    "# 某个变量的某个取值占比超过90%，说明信息含量低，可以删除\n",
    "drop_cols_simple=[]\n",
    "for col in cols:\n",
    "    if max(df_Master[col].value_counts())/len(df_Master)>0.9:\n",
    "        drop_cols_simple.append(col)\n",
    "print(drop_cols_simple)\n",
    "print(len(drop_cols_simple))\n",
    " \n",
    "df_Master = df_Master.drop(drop_cols_simple,axis=1)\n",
    "df_Master.shape\n",
    "df_Master = df_Master.reset_index(drop=True)\n",
    " \n",
    "# 剩下的变量的类型\n",
    "df_Master.dtypes.value_counts()\n",
    " \n",
    "objectcol = df_Master.select_dtypes(include=[\"object\"]).columns\n",
    "numcol = df_Master.select_dtypes(include=[np.float64]).columns\n",
    " \n",
    "# 分类型变量只有12个，我们来看一下这些变量有什么规律\n",
    "df_Master[objectcol]\n",
    " \n",
    " "
   ],
   "id": "c8e567aca3026f7b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "原变量总数： \n",
      " 204\n",
      "排除目标标签和标记训练集和测试集来源的变量总数： \n",
      " 202\n",
      "['WeblogInfo_9', 'WeblogInfo_10', 'WeblogInfo_11', 'WeblogInfo_12', 'WeblogInfo_13', 'WeblogInfo_14', 'UserInfo_21', 'UserInfo_22', 'UserInfo_23', 'UserInfo_24', 'Education_Info1', 'Education_Info2', 'Education_Info3', 'Education_Info4', 'Education_Info5', 'Education_Info6', 'Education_Info7', 'Education_Info8', 'WeblogInfo_23', 'WeblogInfo_25', 'WeblogInfo_26', 'WeblogInfo_28', 'WeblogInfo_29', 'WeblogInfo_31', 'WeblogInfo_32', 'WeblogInfo_34', 'WeblogInfo_35', 'WeblogInfo_37', 'WeblogInfo_38', 'WeblogInfo_39', 'WeblogInfo_40', 'WeblogInfo_41', 'WeblogInfo_42', 'WeblogInfo_43', 'WeblogInfo_44', 'WeblogInfo_45', 'WeblogInfo_46', 'WeblogInfo_47', 'WeblogInfo_48', 'WeblogInfo_49', 'WeblogInfo_50', 'WeblogInfo_51', 'WeblogInfo_52', 'WeblogInfo_53', 'WeblogInfo_54', 'WeblogInfo_55', 'WeblogInfo_56', 'WeblogInfo_57', 'WeblogInfo_58', 'SocialNetwork_1', 'SocialNetwork_2', 'SocialNetwork_14', 'SocialNetwork_15', 'SocialNetwork_16']\n",
      "54\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "      UserInfo_2 UserInfo_4 UserInfo_7 UserInfo_8 UserInfo_9 UserInfo_19  \\\n",
       "0             深圳         深圳         广东         深圳      中国移动          四川省   \n",
       "1             温州         温州         浙江         温州      中国移动          福建省   \n",
       "2             宜昌         宜昌         湖北         宜昌      中国电信          湖北省   \n",
       "3             南平         南平         福建         南平      中国移动          江西省   \n",
       "4             辽阳         辽阳         辽宁         辽阳      中国移动          辽宁省   \n",
       "...          ...        ...        ...        ...        ...         ...   \n",
       "49696       鄂尔多斯       鄂尔多斯        内蒙古       鄂尔多斯      中国联通       内蒙古自治区   \n",
       "49697         聊城         聊城         山东         聊城      中国移动          山东省   \n",
       "49698        秦皇岛        秦皇岛         河北        秦皇岛      中国移动          河北省   \n",
       "49699         广州         茂名         广东         深圳      中国联通          广东省   \n",
       "49700         上海         常州         不详         不详         不详         江西省   \n",
       "\n",
       "      UserInfo_20 WeblogInfo_19 WeblogInfo_20 WeblogInfo_21 ListingInfo  \\\n",
       "0             南充市             I            I5             D    2014/3/5   \n",
       "1              不详             I            I5             D   2014/2/26   \n",
       "2             宜昌市             I            I5             D   2014/2/28   \n",
       "3              不详             I            I5             D   2014/2/25   \n",
       "4             锦州市             I           NaN             D   2014/2/27   \n",
       "...           ...           ...           ...           ...         ...   \n",
       "49696          不详             I            I4             D   26/2/2014   \n",
       "49697          不详             I            I5             D   27/2/2014   \n",
       "49698        秦皇岛市             I            I5             D  16/11/2013   \n",
       "49699         茂名市             I            I5             D   21/2/2014   \n",
       "49700          不详             I            I5             D   28/2/2014   \n",
       "\n",
       "      sample_status  \n",
       "0             train  \n",
       "1             train  \n",
       "2             train  \n",
       "3             train  \n",
       "4             train  \n",
       "...             ...  \n",
       "49696          test  \n",
       "49697          test  \n",
       "49698          test  \n",
       "49699          test  \n",
       "49700          test  \n",
       "\n",
       "[49701 rows x 12 columns]"
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserInfo_2</th>\n",
       "      <th>UserInfo_4</th>\n",
       "      <th>UserInfo_7</th>\n",
       "      <th>UserInfo_8</th>\n",
       "      <th>UserInfo_9</th>\n",
       "      <th>UserInfo_19</th>\n",
       "      <th>UserInfo_20</th>\n",
       "      <th>WeblogInfo_19</th>\n",
       "      <th>WeblogInfo_20</th>\n",
       "      <th>WeblogInfo_21</th>\n",
       "      <th>ListingInfo</th>\n",
       "      <th>sample_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳</td>\n",
       "      <td>深圳</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>四川省</td>\n",
       "      <td>南充市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/3/5</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>温州</td>\n",
       "      <td>温州</td>\n",
       "      <td>浙江</td>\n",
       "      <td>温州</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>福建省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/26</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>湖北</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>中国电信</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>宜昌市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/28</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>南平</td>\n",
       "      <td>南平</td>\n",
       "      <td>福建</td>\n",
       "      <td>南平</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>江西省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/25</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>辽宁省</td>\n",
       "      <td>锦州市</td>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/27</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49696</th>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>内蒙古</td>\n",
       "      <td>鄂尔多斯</td>\n",
       "      <td>中国联通</td>\n",
       "      <td>内蒙古自治区</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I4</td>\n",
       "      <td>D</td>\n",
       "      <td>26/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49697</th>\n",
       "      <td>聊城</td>\n",
       "      <td>聊城</td>\n",
       "      <td>山东</td>\n",
       "      <td>聊城</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>山东省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>27/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49698</th>\n",
       "      <td>秦皇岛</td>\n",
       "      <td>秦皇岛</td>\n",
       "      <td>河北</td>\n",
       "      <td>秦皇岛</td>\n",
       "      <td>中国移动</td>\n",
       "      <td>河北省</td>\n",
       "      <td>秦皇岛市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>16/11/2013</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49699</th>\n",
       "      <td>广州</td>\n",
       "      <td>茂名</td>\n",
       "      <td>广东</td>\n",
       "      <td>深圳</td>\n",
       "      <td>中国联通</td>\n",
       "      <td>广东省</td>\n",
       "      <td>茂名市</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>21/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49700</th>\n",
       "      <td>上海</td>\n",
       "      <td>常州</td>\n",
       "      <td>不详</td>\n",
       "      <td>不详</td>\n",
       "      <td>不详</td>\n",
       "      <td>江西省</td>\n",
       "      <td>不详</td>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>28/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49701 rows × 12 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.451506Z",
     "start_time": "2024-09-27T12:59:50.430261Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可以看到的是\n",
    "# 表示省份的有\n",
    "# UserInfo_19和UserInfo_7\n",
    "# 表示城市的有\n",
    "# UserInfo_2,UserInfo_20,UserInfo_4,UserInfo_8\n",
    "city_feature = ['UserInfo_2','UserInfo_20','UserInfo_4','UserInfo_8']\n",
    "province_feature=['UserInfo_7','UserInfo_19']\n",
    " \n",
    "print(\"城市特征：\")\n",
    "for col in city_feature:\n",
    "    print(col,\":\",df_Master[col].nunique())\n",
    " \n",
    "print('\\n')\n",
    "print(\"省份特征：\")\n",
    "for col in province_feature:\n",
    "        print(col,\":\",df_Master[col].nunique())"
   ],
   "id": "17103951978c10be",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "城市特征：\n",
      "UserInfo_2 : 329\n",
      "UserInfo_20 : 307\n",
      "UserInfo_4 : 332\n",
      "UserInfo_8 : 664\n",
      "\n",
      "\n",
      "省份特征：\n",
      "UserInfo_7 : 32\n",
      "UserInfo_19 : 31\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.460976Z",
     "start_time": "2024-09-27T12:59:50.453505Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(df_Master.UserInfo_8.unique()[:50])\n",
    "# 可以看到，同一个城市表达不一"
   ],
   "id": "69b07291fba74491",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['深圳' '温州' '宜昌' '南平' '辽阳' '不详' '包头' '赤峰' '鄂州' '武汉' '长沙' '漳州' '牡丹江' '太原市'\n",
      " '北京' '忻州' '三明' '临沂' '福州' '泰州市' '大同' '红河' '郴州' '常州' '湖州' '佛山' '天津' '南宁'\n",
      " '聊城' '柳州' '广州市' '太原' '重庆' '杭州' '景德镇' '上饶' '鸡西' '资阳' '成都' '济宁' '滨州' '渭南'\n",
      " '广州' '都匀' '廊坊' '西宁市' '金华' '龙岩' '清远' '兰州']\n"
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.508999Z",
     "start_time": "2024-09-27T12:59:50.461979Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 去掉字段中的“市”，保持统一\n",
    "df_Master['UserInfo_8'] = [a[:-1] if a.find('市')!= -1 else a[:] for a in df_Master['UserInfo_8']]\n",
    " \n",
    "# 清理后非重复计数减小\n",
    "df_Master['UserInfo_8'].nunique()\n",
    " \n",
    " \n",
    "# 再来看看数值型变量\n",
    "df_Master[numcol].head(20)\n",
    "# 这里我们不对数值变量进行缺失值插值或者填充，直接用于后期建模\n",
    " \n",
    "# 再来看看其他的表——该表显示了客户修改信息的日志\n",
    "df_Userupdate"
   ],
   "id": "778e00cb7e5e5932",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Idx ListingInfo1    UserupdateInfo1 UserupdateInfo2\n",
       "0       10001   2014/03/05       _EducationId      2014/02/20\n",
       "1       10001   2014/03/05         _HasBuyCar      2014/02/20\n",
       "2       10001   2014/03/05    _LastUpdateDate      2014/02/20\n",
       "3       10001   2014/03/05  _MarriageStatusId      2014/02/20\n",
       "4       10001   2014/03/05       _MobilePhone      2014/02/20\n",
       "...       ...          ...                ...             ...\n",
       "621290   9994   2014/02/28                _QQ      2014/02/20\n",
       "621291   9994   2014/02/28  _ResidenceAddress      2014/02/20\n",
       "621292   9994   2014/02/28    _ResidencePhone      2014/02/20\n",
       "621293   9994   2014/02/28   _ResidenceTypeId      2014/02/20\n",
       "621294   9994   2014/02/28    _ResidenceYears      2014/02/20\n",
       "\n",
       "[621295 rows x 4 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>ListingInfo1</th>\n",
       "      <th>UserupdateInfo1</th>\n",
       "      <th>UserupdateInfo2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_EducationId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_HasBuyCar</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_LastUpdateDate</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MarriageStatusId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10001</td>\n",
       "      <td>2014/03/05</td>\n",
       "      <td>_MobilePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621290</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_QQ</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621291</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceAddress</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621292</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidencePhone</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621293</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceTypeId</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621294</th>\n",
       "      <td>9994</td>\n",
       "      <td>2014/02/28</td>\n",
       "      <td>_ResidenceYears</td>\n",
       "      <td>2014/02/20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>621295 rows × 4 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.687275Z",
     "start_time": "2024-09-27T12:59:50.510003Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将上表的大小写进行统一\n",
    "df_Userupdate['UserupdateInfo1'] = df_Userupdate.UserupdateInfo1.map(lambda s:s.lower())\n",
    " \n",
    " \n",
    "######################################特征工程阶段#######################################\n",
    "# 至此，我们进入特征处理阶段\n",
    "# 首先对类别变量进行变换\n",
    "df_Master[objectcol]\n",
    " \n",
    " \n",
    "# 1)省份特征————————推测可能一个是籍贯省份，一个是居住省份\n",
    "# 首先看看各省份好坏样本的分布占比\n",
    "def get_badrate(df,col):\n",
    "    '''\n",
    "    根据某个变量计算违约率\n",
    "    '''\n",
    "    group = df.groupby(col)\n",
    "    df=pd.DataFrame()\n",
    "    df['total'] = group.target.count()\n",
    "    df['bad'] = group.target.sum()\n",
    "    df['badrate'] = round(df['bad']/df['total'],4)*100  # 百分比形式\n",
    "    return df.sort_values('badrate',ascending=False)\n",
    " \n",
    "# 户籍省份的违约率计算\n",
    "province_original = get_badrate(df_Master,'UserInfo_19')\n",
    "province_original "
   ],
   "id": "a863b1d3ec6745d8",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             total    bad  badrate\n",
       "UserInfo_19                       \n",
       "天津市            137   17.0    12.41\n",
       "山东省           2366  256.0    10.82\n",
       "吉林省            498   47.0     9.44\n",
       "黑龙江省           813   71.0     8.73\n",
       "湖南省           1753  149.0     8.50\n",
       "辽宁省            683   58.0     8.49\n",
       "四川省           1833  155.0     8.46\n",
       "湖北省           1635  132.0     8.07\n",
       "海南省            162   13.0     8.02\n",
       "河北省           1202   96.0     7.99\n",
       "江苏省           1817  145.0     7.98\n",
       "安徽省           1452  111.0     7.64\n",
       "重庆市            389   29.0     7.46\n",
       "陕西省            691   49.0     7.09\n",
       "河南省           1806  126.0     6.98\n",
       "上海市            189   13.0     6.88\n",
       "贵州省            663   45.0     6.79\n",
       "江西省           1146   77.0     6.72\n",
       "广东省           2393  160.0     6.69\n",
       "山西省            942   57.0     6.05\n",
       "福建省           2009  110.0     5.48\n",
       "甘肃省            535   29.0     5.42\n",
       "广西壮族自治区       1195   64.0     5.36\n",
       "浙江省           1677   86.0     5.13\n",
       "云南省            678   34.0     5.01\n",
       "内蒙古自治区         567   28.0     4.94\n",
       "北京市            128    6.0     4.69\n",
       "宁夏回族自治区        202    9.0     4.46\n",
       "青海省             68    3.0     4.41\n",
       "新疆维吾尔自治区       202    7.0     3.47\n",
       "西藏自治区            1    0.0     0.00"
      ],
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_19</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>137</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>2366</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>498</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>813</td>\n",
       "      <td>71.0</td>\n",
       "      <td>8.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>1753</td>\n",
       "      <td>149.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁省</th>\n",
       "      <td>683</td>\n",
       "      <td>58.0</td>\n",
       "      <td>8.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川省</th>\n",
       "      <td>1833</td>\n",
       "      <td>155.0</td>\n",
       "      <td>8.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北省</th>\n",
       "      <td>1635</td>\n",
       "      <td>132.0</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南省</th>\n",
       "      <td>162</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北省</th>\n",
       "      <td>1202</td>\n",
       "      <td>96.0</td>\n",
       "      <td>7.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏省</th>\n",
       "      <td>1817</td>\n",
       "      <td>145.0</td>\n",
       "      <td>7.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽省</th>\n",
       "      <td>1452</td>\n",
       "      <td>111.0</td>\n",
       "      <td>7.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆市</th>\n",
       "      <td>389</td>\n",
       "      <td>29.0</td>\n",
       "      <td>7.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西省</th>\n",
       "      <td>691</td>\n",
       "      <td>49.0</td>\n",
       "      <td>7.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南省</th>\n",
       "      <td>1806</td>\n",
       "      <td>126.0</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海市</th>\n",
       "      <td>189</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州省</th>\n",
       "      <td>663</td>\n",
       "      <td>45.0</td>\n",
       "      <td>6.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西省</th>\n",
       "      <td>1146</td>\n",
       "      <td>77.0</td>\n",
       "      <td>6.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东省</th>\n",
       "      <td>2393</td>\n",
       "      <td>160.0</td>\n",
       "      <td>6.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西省</th>\n",
       "      <td>942</td>\n",
       "      <td>57.0</td>\n",
       "      <td>6.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建省</th>\n",
       "      <td>2009</td>\n",
       "      <td>110.0</td>\n",
       "      <td>5.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃省</th>\n",
       "      <td>535</td>\n",
       "      <td>29.0</td>\n",
       "      <td>5.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西壮族自治区</th>\n",
       "      <td>1195</td>\n",
       "      <td>64.0</td>\n",
       "      <td>5.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江省</th>\n",
       "      <td>1677</td>\n",
       "      <td>86.0</td>\n",
       "      <td>5.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南省</th>\n",
       "      <td>678</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古自治区</th>\n",
       "      <td>567</td>\n",
       "      <td>28.0</td>\n",
       "      <td>4.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京市</th>\n",
       "      <td>128</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏回族自治区</th>\n",
       "      <td>202</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海省</th>\n",
       "      <td>68</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆维吾尔自治区</th>\n",
       "      <td>202</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏自治区</th>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.707601Z",
     "start_time": "2024-09-27T12:59:50.689274Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 居住地省份的违约率计算\n",
    "province_current = get_badrate(df_Master,'UserInfo_7')\n",
    "province_current "
   ],
   "id": "1ae59d9f3d8c4507",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            total    bad  badrate\n",
       "UserInfo_7                       \n",
       "山东           2059  226.0    10.98\n",
       "天津            183   20.0    10.93\n",
       "四川           1117  108.0     9.67\n",
       "湖南           1056  100.0     9.47\n",
       "海南            164   15.0     9.15\n",
       "辽宁            557   48.0     8.62\n",
       "吉林            295   25.0     8.47\n",
       "江苏           1722  139.0     8.07\n",
       "湖北           1041   84.0     8.07\n",
       "不详           4187  333.0     7.95\n",
       "安徽            883   68.0     7.70\n",
       "河北            901   69.0     7.66\n",
       "重庆            457   34.0     7.44\n",
       "广东           3866  287.0     7.42\n",
       "江西            642   46.0     7.17\n",
       "上海             43    3.0     6.98\n",
       "黑龙江           430   30.0     6.98\n",
       "河南           1233   82.0     6.65\n",
       "陕西            523   32.0     6.12\n",
       "山西            778   47.0     6.04\n",
       "贵州            427   25.0     5.85\n",
       "福建           1880  105.0     5.59\n",
       "甘肃            287   15.0     5.23\n",
       "浙江           2182  113.0     5.18\n",
       "广西            848   43.0     5.07\n",
       "北京            639   32.0     5.01\n",
       "宁夏            164    8.0     4.88\n",
       "内蒙古           391   19.0     4.86\n",
       "青海             52    2.0     3.85\n",
       "云南            635   21.0     3.31\n",
       "新疆            179    3.0     1.68\n",
       "西藏             11    0.0     0.00"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_7</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>山东</th>\n",
       "      <td>2059</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>183</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>1117</td>\n",
       "      <td>108.0</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南</th>\n",
       "      <td>1056</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南</th>\n",
       "      <td>164</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>辽宁</th>\n",
       "      <td>557</td>\n",
       "      <td>48.0</td>\n",
       "      <td>8.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林</th>\n",
       "      <td>295</td>\n",
       "      <td>25.0</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江苏</th>\n",
       "      <td>1722</td>\n",
       "      <td>139.0</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北</th>\n",
       "      <td>1041</td>\n",
       "      <td>84.0</td>\n",
       "      <td>8.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>不详</th>\n",
       "      <td>4187</td>\n",
       "      <td>333.0</td>\n",
       "      <td>7.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>安徽</th>\n",
       "      <td>883</td>\n",
       "      <td>68.0</td>\n",
       "      <td>7.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河北</th>\n",
       "      <td>901</td>\n",
       "      <td>69.0</td>\n",
       "      <td>7.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>重庆</th>\n",
       "      <td>457</td>\n",
       "      <td>34.0</td>\n",
       "      <td>7.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广东</th>\n",
       "      <td>3866</td>\n",
       "      <td>287.0</td>\n",
       "      <td>7.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>江西</th>\n",
       "      <td>642</td>\n",
       "      <td>46.0</td>\n",
       "      <td>7.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>上海</th>\n",
       "      <td>43</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江</th>\n",
       "      <td>430</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>河南</th>\n",
       "      <td>1233</td>\n",
       "      <td>82.0</td>\n",
       "      <td>6.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>陕西</th>\n",
       "      <td>523</td>\n",
       "      <td>32.0</td>\n",
       "      <td>6.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山西</th>\n",
       "      <td>778</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>贵州</th>\n",
       "      <td>427</td>\n",
       "      <td>25.0</td>\n",
       "      <td>5.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>福建</th>\n",
       "      <td>1880</td>\n",
       "      <td>105.0</td>\n",
       "      <td>5.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>甘肃</th>\n",
       "      <td>287</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>浙江</th>\n",
       "      <td>2182</td>\n",
       "      <td>113.0</td>\n",
       "      <td>5.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>广西</th>\n",
       "      <td>848</td>\n",
       "      <td>43.0</td>\n",
       "      <td>5.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>北京</th>\n",
       "      <td>639</td>\n",
       "      <td>32.0</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>宁夏</th>\n",
       "      <td>164</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>内蒙古</th>\n",
       "      <td>391</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>青海</th>\n",
       "      <td>52</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>云南</th>\n",
       "      <td>635</td>\n",
       "      <td>21.0</td>\n",
       "      <td>3.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>新疆</th>\n",
       "      <td>179</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西藏</th>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.717241Z",
     "start_time": "2024-09-27T12:59:50.708601Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 各取前5名的省份进行二值化\n",
    "province_original.iloc[:5,]"
   ],
   "id": "8e1a044ae4eb05be",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "             total    bad  badrate\n",
       "UserInfo_19                       \n",
       "天津市            137   17.0    12.41\n",
       "山东省           2366  256.0    10.82\n",
       "吉林省            498   47.0     9.44\n",
       "黑龙江省           813   71.0     8.73\n",
       "湖南省           1753  149.0     8.50"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_19</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>天津市</th>\n",
       "      <td>137</td>\n",
       "      <td>17.0</td>\n",
       "      <td>12.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>山东省</th>\n",
       "      <td>2366</td>\n",
       "      <td>256.0</td>\n",
       "      <td>10.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>吉林省</th>\n",
       "      <td>498</td>\n",
       "      <td>47.0</td>\n",
       "      <td>9.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>黑龙江省</th>\n",
       "      <td>813</td>\n",
       "      <td>71.0</td>\n",
       "      <td>8.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南省</th>\n",
       "      <td>1753</td>\n",
       "      <td>149.0</td>\n",
       "      <td>8.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:50.728664Z",
     "start_time": "2024-09-27T12:59:50.719241Z"
    }
   },
   "cell_type": "code",
   "source": "province_current.iloc[:5,]",
   "id": "3c3d785aa8d4a14e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "            total    bad  badrate\n",
       "UserInfo_7                       \n",
       "山东           2059  226.0    10.98\n",
       "天津            183   20.0    10.93\n",
       "四川           1117  108.0     9.67\n",
       "湖南           1056  100.0     9.47\n",
       "海南            164   15.0     9.15"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total</th>\n",
       "      <th>bad</th>\n",
       "      <th>badrate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UserInfo_7</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>山东</th>\n",
       "      <td>2059</td>\n",
       "      <td>226.0</td>\n",
       "      <td>10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天津</th>\n",
       "      <td>183</td>\n",
       "      <td>20.0</td>\n",
       "      <td>10.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>四川</th>\n",
       "      <td>1117</td>\n",
       "      <td>108.0</td>\n",
       "      <td>9.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖南</th>\n",
       "      <td>1056</td>\n",
       "      <td>100.0</td>\n",
       "      <td>9.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>海南</th>\n",
       "      <td>164</td>\n",
       "      <td>15.0</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:58.973498Z",
     "start_time": "2024-09-27T12:59:50.729670Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 分别对户籍省份和居住省份排名前五的省份进行二值化\n",
    "# 户籍省份的二值化\n",
    "df_Master['is_tianjin_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='天津市' else 0,axis=1)\n",
    "df_Master['is_shandong_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='山东省' else 0,axis=1)\n",
    "df_Master['is_jilin_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='吉林省' else 0,axis=1)\n",
    "df_Master['is_heilongjiang_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='黑龙江省' else 0,axis=1)\n",
    "df_Master['is_hunan_UserInfo_19']=df_Master.apply(lambda x:1 if x.UserInfo_19=='湖南省' else 0,axis=1)\n",
    " \n",
    "# 居住省份的二值化\n",
    "df_Master['is_tianjin_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='天津' else 0,axis=1)\n",
    "df_Master['is_shandong_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='山东' else 0,axis=1)\n",
    "df_Master['is_sichuan_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='四川' else 0,axis=1)\n",
    "df_Master['is_hainan_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='海南' else 0,axis=1)\n",
    "df_Master['is_hunan_UserInfo_7']=df_Master.apply(lambda x:1 if x.UserInfo_7=='湖南' else 0,axis=1)\n",
    " \n",
    " \n",
    "# 户籍省份和居住地省份不一致的特征衍生\n",
    "print(df_Master.UserInfo_19.unique())\n",
    "print('\\n')\n",
    "print(df_Master.UserInfo_7.unique())\n",
    " \n",
    " \n",
    "# 首先将两者改成相同的形式\n",
    "UserInfo_19_change = []\n",
    "for i in df_Master.UserInfo_19:\n",
    "    if i in ('内蒙古自治区','黑龙江省'):\n",
    "        j = i[:3]\n",
    "    else:\n",
    "        j = i[:2]\n",
    "    UserInfo_19_change.append(j)\n",
    "print(np.unique(UserInfo_19_change))\n",
    " \n",
    " \n",
    "# 判断UserInfo_7和UserInfo_19是否一致\n",
    "is_same_province=[]\n",
    "for i,j in zip(df_Master.UserInfo_7,UserInfo_19_change):\n",
    "    if i==j:\n",
    "        a=1\n",
    "    else:\n",
    "        a=0\n",
    "    is_same_province.append(a)\n",
    "df_Master['is_same_province'] = is_same_province"
   ],
   "id": "9ce1b494ea4b7625",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['四川省' '福建省' '湖北省' '江西省' '辽宁省' '山东省' '内蒙古自治区' '湖南省' '黑龙江省' '山西省' '江苏省'\n",
      " '云南省' '浙江省' '广东省' '天津市' '广西壮族自治区' '甘肃省' '贵州省' '陕西省' '重庆市' '河北省' '青海省'\n",
      " '安徽省' '上海市' '吉林省' '北京市' '河南省' '宁夏回族自治区' '新疆维吾尔自治区' '海南省' '西藏自治区']\n",
      "\n",
      "\n",
      "['广东' '浙江' '湖北' '福建' '辽宁' '不详' '内蒙古' '湖南' '黑龙江' '山西' '北京' '山东' '江苏' '云南'\n",
      " '天津' '广西' '重庆' '江西' '四川' '陕西' '贵州' '河北' '青海' '甘肃' '安徽' '吉林' '新疆' '海南'\n",
      " '河南' '宁夏' '上海' '西藏']\n",
      "['上海' '云南' '内蒙古' '北京' '吉林' '四川' '天津' '宁夏' '安徽' '山东' '山西' '广东' '广西' '新疆'\n",
      " '江苏' '江西' '河北' '河南' '浙江' '海南' '湖北' '湖南' '甘肃' '福建' '西藏' '贵州' '辽宁' '重庆'\n",
      " '陕西' '青海' '黑龙江']\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T12:59:58.987300Z",
     "start_time": "2024-09-27T12:59:58.975495Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2)城市特征\n",
    "# 原数据中有四个城市特征,推测为用户常登陆的IP地址城市\n",
    "# 特征衍生思路:\n",
    "# 一,通过xgboost挑选重要的城市,进行二值化\n",
    "# 二,由四个城市特征的非重复计数衍生生成登陆IP地址的变更次数\n",
    " \n",
    "# 根据xgboost变量重要性的输出对城市作二值化衍生\n",
    "df_Master_temp = df_Master[['UserInfo_2','UserInfo_4','UserInfo_8','UserInfo_20','target']]\n",
    "df_Master_temp.head()"
   ],
   "id": "887f61b4c0a70977",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "  UserInfo_2 UserInfo_4 UserInfo_8 UserInfo_20  target\n",
       "0         深圳         深圳         深圳         南充市     0.0\n",
       "1         温州         温州         温州          不详     0.0\n",
       "2         宜昌         宜昌         宜昌         宜昌市     0.0\n",
       "3         南平         南平         南平          不详     0.0\n",
       "4         辽阳         辽阳         辽阳         锦州市     0.0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UserInfo_2</th>\n",
       "      <th>UserInfo_4</th>\n",
       "      <th>UserInfo_8</th>\n",
       "      <th>UserInfo_20</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>深圳</td>\n",
       "      <td>深圳</td>\n",
       "      <td>深圳</td>\n",
       "      <td>南充市</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>温州</td>\n",
       "      <td>温州</td>\n",
       "      <td>温州</td>\n",
       "      <td>不详</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌</td>\n",
       "      <td>宜昌市</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>南平</td>\n",
       "      <td>南平</td>\n",
       "      <td>南平</td>\n",
       "      <td>不详</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>辽阳</td>\n",
       "      <td>锦州市</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:00.287185Z",
     "start_time": "2024-09-27T12:59:58.989298Z"
    }
   },
   "cell_type": "code",
   "source": [
    "area_list=[]\n",
    "# 将四个城市特征都进行哑变量处理\n",
    "for col in df_Master_temp:\n",
    "    dummy_df = pd.get_dummies(df_Master_temp[col])\n",
    "    dummy_df = dummy_df.astype(int)\n",
    "    dummy_df = pd.concat([dummy_df,df_Master_temp['target']],axis=1)\n",
    "    area_list.append(dummy_df)\n",
    "df_area1 = area_list[0]\n",
    "df_area2 = area_list[1]\n",
    "df_area3 = area_list[2]\n",
    "df_area4 = area_list[3]\n",
    " \n",
    "df_area1"
   ],
   "id": "d3e9b334e85f0332",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       七台河  三明  三门峡  上海  上饶  东莞  东营  中山  临夏回族自治州  临汾  临沂  临沧  丹东  丽水  丽江  \\\n",
       "0        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "1        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "2        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "3        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "4        0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "...    ...  ..  ...  ..  ..  ..  ..  ..      ...  ..  ..  ..  ..  ..  ..   \n",
       "49696    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49697    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49698    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49699    0   0    0   0   0   0   0   0        0   0   0   0   0   0   0   \n",
       "49700    0   0    0   1   0   0   0   0        0   0   0   0   0   0   0   \n",
       "\n",
       "       乌兰察布盟  乌海  乌鲁木齐  乐山  九江  云浮  亳州  伊春  伊犁哈萨克自治州  佛山  佳木斯  保定  保山  信阳  \\\n",
       "0          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "1          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "2          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "3          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "4          0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "...      ...  ..   ...  ..  ..  ..  ..  ..       ...  ..  ...  ..  ..  ..   \n",
       "49696      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49697      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49698      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49699      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "49700      0   0     0   0   0   0   0   0         0   0    0   0   0   0   \n",
       "\n",
       "       克孜勒苏柯尔克孜自治州  克拉玛依  六安  六盘水  兰州  兴安盟  内江  凉山  包头  北京  北海  十堰  南京  南充  \\\n",
       "0                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "1                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "2                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "3                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "4                0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "...            ...   ...  ..  ...  ..  ...  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "49696            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49697            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49698            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49699            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "49700            0     0   0    0   0    0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "       南宁  南平  南昌  南通  南阳  博尔塔拉蒙古自治州  厦门  双鸭山  台州  合肥  吉安  吉林市  吐鲁番  吕梁  吴忠  \\\n",
       "0       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "1       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "2       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "3       0   1   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "4       0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "...    ..  ..  ..  ..  ..        ...  ..  ...  ..  ..  ..  ...  ...  ..  ..   \n",
       "49696   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49697   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49698   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49699   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "49700   0   0   0   0   0          0   0    0   0   0   0    0    0   0   0   \n",
       "\n",
       "       周口  呼伦贝尔  呼和浩特  和田  咸宁  咸阳  哈密  哈尔滨  唐山  商丘  商洛  喀什  嘉兴  嘉峪关  四平  固原  \\\n",
       "0       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "1       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "2       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "3       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "4       0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "...    ..   ...   ...  ..  ..  ..  ..  ...  ..  ..  ..  ..  ..  ...  ..  ..   \n",
       "49696   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49697   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49698   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49699   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "49700   0     0     0   0   0   0   0    0   0   0   0   0   0    0   0   0   \n",
       "\n",
       "       大兴安岭  大同  大庆  大理白族自治州  大连  天水  天津  太原  威海  娄底  孝感  宁德  宁波  安庆  安康  安阳  \\\n",
       "0         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "1         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "2         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "3         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "4         0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "...     ...  ..  ..      ...  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..   \n",
       "49696     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49697     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49698     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49699     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "49700     0   0   0        0   0   0   0   0   0   0   0   0   0   0   0   0   \n",
       "\n",
       "       安顺  定西  宜宾  宜昌  宜春  宝鸡  宣城  宿州  宿迁  山南  岳阳  崇左  巢湖  巴中  巴彦淖尔盟  \\\n",
       "0       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "1       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "2       0   0   0   1   0   0   0   0   0   0   0   0   0   0      0   \n",
       "3       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "4       0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..  ..    ...   \n",
       "49696   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49697   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49698   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49699   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "49700   0   0   0   0   0   0   0   0   0   0   0   0   0   0      0   \n",
       "\n",
       "       巴音郭楞蒙古自治州  常州  常德  平凉  平顶山  广元  广安  广州  庆阳  ...  珠海  甘南藏族自治州  甘孜藏族自治州  \\\n",
       "0              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "1              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "2              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "3              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "4              0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "...          ...  ..  ..  ..  ...  ..  ..  ..  ..  ...  ..      ...      ...   \n",
       "49696          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "49697          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "49698          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "49699          0   0   0   0    0   0   0   1   0  ...   0        0        0   \n",
       "49700          0   0   0   0    0   0   0   0   0  ...   0        0        0   \n",
       "\n",
       "       白城  白山  白银  百色  益阳  盐城  盘锦  眉山  石嘴山  石家庄  石河子  福州  秦皇岛  红河哈尼族彝族自治州  绍兴  \\\n",
       "0       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "1       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "2       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "3       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "4       0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "...    ..  ..  ..  ..  ..  ..  ..  ..  ...  ...  ...  ..  ...         ...  ..   \n",
       "49696   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "49697   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "49698   0   0   0   0   0   0   0   0    0    0    0   0    1           0   0   \n",
       "49699   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "49700   0   0   0   0   0   0   0   0    0    0    0   0    0           0   0   \n",
       "\n",
       "       绥化  绵阳  聊城  肇庆  自贡  舟山  芜湖  苏州  茂名  荆州  荆门  莆田  莱芜  菏泽  萍乡  营口  葫芦岛  \\\n",
       "0       0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   \n",
       "1       0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   \n",
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       "3       0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0    0   \n",
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       "      <th></th>\n",
       "      <th>七台河</th>\n",
       "      <th>三明</th>\n",
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       "      <th>克拉玛依</th>\n",
       "      <th>六安</th>\n",
       "      <th>六盘水</th>\n",
       "      <th>兰州</th>\n",
       "      <th>兴安盟</th>\n",
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       "      <th>大连</th>\n",
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       "    </tr>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "    <tr>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49701 rows × 330 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:11.280423Z",
     "start_time": "2024-09-27T13:00:00.295170Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 使用xgboost筛选出重要的城市\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "from xgboost import plot_importance\n",
    " \n",
    " \n",
    "# 注意,这里需要把合并后的没有目标标签的行数据删除\n",
    "# df_area1[~(df_area1['target'].isnull())]\n",
    " \n",
    " \n",
    "x_area1 = df_area1[~(df_area1['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area1 = df_area1[~(df_area1['target'].isnull())]['target']\n",
    "x_area2 = df_area2[~(df_area2['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area2 = df_area2[~(df_area2['target'].isnull())]['target']\n",
    "x_area3 = df_area3[~(df_area3['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area3 = df_area3[~(df_area3['target'].isnull())]['target']\n",
    "x_area4 = df_area4[~(df_area4['target'].isnull())].drop(['target'],axis=1)\n",
    "y_area4 = df_area4[~(df_area4['target'].isnull())]['target']\n",
    " \n",
    " \n",
    " \n",
    "xg_area1 = XGBClassifier(random_state=0).fit(x_area1,y_area1)\n",
    "xg_area2 = XGBClassifier(random_state=0).fit(x_area2,y_area2)\n",
    "xg_area3 = XGBClassifier(random_state=0).fit(x_area3,y_area3)\n",
    "xg_area4 = XGBClassifier(random_state=0).fit(x_area4,y_area4)\n",
    " \n",
    " \n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']\n",
    "fig = plt.figure(figsize=(20,8))\n",
    "ax1 = fig.add_subplot(2,2,1)\n",
    "ax2 = fig.add_subplot(2,2,2)\n",
    "ax3 = fig.add_subplot(2,2,3)\n",
    "ax4 = fig.add_subplot(2,2,4)\n",
    " \n",
    "plot_importance(xg_area1,ax=ax1,max_num_features=10,height=0.4)\n",
    "plot_importance(xg_area2,ax=ax2,max_num_features=10,height=0.4)\n",
    "plot_importance(xg_area3,ax=ax3,max_num_features=10,height=0.4)\n",
    "plot_importance(xg_area4,ax=ax4,max_num_features=10,height=0.4)"
   ],
   "id": "95000ad55fbcd54e",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Feature importance'}, xlabel='F score', ylabel='Features'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 4 Axes>"
      ],
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ntHv3bsXGxmrGjBkptkVHR1st6eDm5qbg4GAdPHgwK0OEE8iMuunLL79UTEyMTp48qfLly6tGjRry9PSUm5ubChcunOo+JUuW1Pz589WlSxeVLFlSGzdulNFo1Pvvv6/3338/1X0mTJigV155Jd3nRs0EAAByC2oDOCubG0AXLlxQtWrVUox7e3vr+vXrGTpWzZo1NWbMGJlMJp0/f16SLA9H/Suj0aj9+/crKChI169fV+HChVW5cmWtXbtWY8eOlYeHh5KTk+XqatfKdgAAAIDmz5+vli1bpviEn/RwmYcSJUpYjfn6+uro0aNZFB2cRWbUTY/u2gkICNDGjRslSbNnz9atW7dUvXp1JSUlSZJmzZolSXrllVc0YcIEValSRQsXLtSvv/6qTZs2aeXKlXrhhRcy4awAAAByF2oDOCubG0B+fn767rvvVLFiRUmSi4uLJGn9+vWpNm/Scv36deXLl08tWrSQJE2ePFlGo1GDBg1KMffevXvq1KmTjh07pv79+yt//vz6+OOPVaxYMY0bN06SlJycLDc3m08LAADAaZlMJkeH4LQeLZ/36L/nzp3Trl27tG7dulSX1rt//748PT2ttnl6eiohIcEpl+JzxpidRWbVTX/31VdfaebMmapfv77Gjx+vggULqkePHpbt69evV4UKFRQQEKBPPvlEERERateunZ555pkUx7p7964WLlyokJAQm+MBAADOh/eAKf29LpByX22A1Dnrz9LmTsnw4cPVtWtXnTp1SiaTSQsWLNDp06f166+/atKkSek+ztSpU3Xo0CFNnTo1QwXQ3Llz9fHHH6t58+YaPXq0GjduLEmKj49Xnjx5Mnw+AAAAzu7EiROW54vANjExMZIePp+lXLlyio+P14EDB1LMc3Nz06lTp6y2nTt3TpJSnY/cKzPqph07dujXX3+V0WjUxIkTVaNGDfXt29dqqZG/K1CggHr16qWOHTtq8eLF+vXXX1WtWjWtWLFCc+fOlZubm9566y25uLioTZs28vDwyKxTBgAATuLYsWPUD2l4VBdI1AZwbjY3gKpXr67Nmzdr9uzZqlSpkmJiYlS2bFmtXr3aam3rJ3n//ff1wQcfqFOnTtq+fXu69/Px8dHEiRO1YsUKzZkzR6+88ooMBoMePHggLy8vW04JAADAqVWoUIGlcG1kNBoVExOjgIAAGQwGDRkyRB06dEh16S5JKl68uNzd3a22r1ixQhUqVEhzn+zs0fkj82VG3fTcc8/JaDTK1dVV1apVU8mSJdW5c2d98MEHkpRiCThJ2rRpk9asWaOffvpJXl5eOn36tJ5//nlJ0ptvvqlPP/1Uv//+uwICApSYmCh3d/dMPnMAAJDd+fn5OTqEbOfvdYGkXFcbIHXOWjPZ3AAaMGCAevbsqX//+992BeDh4aHx48fr5MmT8vT0zPD+r7/+utq1a2f5H/Lu3bvcAQQAAHIlV1dXy3si2MZgMOj48eOKi4tTo0aN0sxnUFCQdu/erQEDBkh6WAzs3btXb7/9Nj8DWMmMusnPz09+fn5ycXHRK6+8Iunhw4alhw8k7tatm9zc3LR27VqVK1fOat927dqpcePGunjxoiRp27Ztlm2bNm2S9HCp7fDwcLVs2VIff/yxzXECAADnwvvWtBkMBhkMBh05coTaAE7N5gbQr7/+qsTExEwLJDk5Od1zu3btqr1796YYN5vNMhqN6tChQ4ptAwcOVJcuXewJEQAAALlAdHS0ChcubLlbQnr4fKXu3burQ4cOCgsLU3h4uNq1a6fp06crLCxMy5cvl8lkUuvWrR0YObKjzK6b/ioxMVETJkzQK6+8omeeeUaff/65pk2bZtl+8OBBLV68WFu3blXPnj3VvHlzvfbaa1q6dKmaN29ueYhx/fr1tXz58hQPLwYAAMjtqA3g7GxuAI0ZM0YzZsxQr169FBAQYNea0XFxcWrVqpV2796drvkLFy5MdXzSpEn673//q6+//trmWB4pW8RbCc75XCcAAJDLlC/q4+gQcpTDhw+rcuXKVmPJyck6ffq0rly5Ikny9/dXZGSkIiMjNWfOHAUGBmrhwoXy8eFnAWuZWTdJ0g8//KDff/9dffv21eTJk1WjRg25u7urYMGCOnfunNatW6c2bdpIkvbv36/Lly9Lki5cuKDixYtLki5fvqw333xTixYtUsGCBZWUlGTTEnDUTAAAOCfqh/SjNoCzs7kB1KdPH5nNZv3000+SUt4y+Pvvv6f7WD/99JMCAwNVsGBBW8PRqlWr9MUXX8jHx0e9e/fWe++9pwoVKth8vPFtq3KLHgAAcBpJyUa5ujg6ipxhwoQJKcY8PDy0Y8cOq7EmTZqoSZMmWRUWnFRm1E2nTp3SV199peTkZH3wwQfq1q2bVq1apa1bt+rrr7/WjBkzJEl9+/bV//3f/6lEiRKqVauWYmJiVKNGDUkPG0DFihWTJA0aNEg+Pj6Wps+DBw9sagBRMwEA4LyMJrMMFBBPRG0AZ2dzA2j06NFyccmcvySioqL00ksvWV6bzWarJeFMJpOSk5NlNKb8eNnvv/+uKVOmaP/+/ZoyZYpq1aqlefPmqX379mrSpIkiIiLk6+ub4ZiMRiPFjA2MRqOOHTsmPz8/8mcD8mcf8mcf8mcf8mcf8mcfo9GoI7GHVaVKFUeHAuBvMqNumjFjhv78809NmzZNL730kpYtW6bx48dr2bJlVp8s9fX11bhx49S7d2+NHz9e+/btszx7yMPDQ//3f/9nddwvvvhCknT//n01bdpUy5cvT/EMocehZkJG8fsetuC6gS24bp6M5g+QO9jcAHq0rIC9bty4oV27dmngwIGWsfnz52v+/PmW139/dk9cXJyioqK0ceNGxcbGqlmzZtq8ebOKFi0qSXr33Xf1+uuva9y4cWrcuLE6deqkAQMGyMvLK1NixuPFx8c7OgSnRv7sQ/7sQ/7sQ/7sQ/7sk5SU5OgQAKQiM+qmzz77TG5uD0u3xMREbd26VTNnzpS/v3+KufXq1dN7772nCxcu6PLly6pWrZqkh+vXP5KQkKD79+/L29tb27Zt06RJkxQVFWV3nEB68PsetuC6gS24bgDAjgbQN99889jtzZs3T9dxYmNjVaJECfn5+Ul62Lx59913U517+/ZtBQcHa/PmzVq/fr2aNWumKVOmpHqHT/HixfX5559r9+7d+vrrr+Xp6ZmueAAAAAAgs2RG3fSo+SM9vJNn+fLlj53fqVMnbd68WRUqVFCBAgVSbL9w4YKaNWsmk8mkIkWKaMSIEU+MAQAAAIDzsbkBNHjw4FTHHy1vkN4GUN26dfXVV1+la+4zzzyjY8eOSZJ69OiRrn1CQ0MVGhqarrkAAAAAkJkyq256nKFDh6YYe/XVV/Xyyy+nOr9s2bI6cuSITCaTXF1d7f7+AAAAALInmxtAR48etXptNpt15swZff755xle5oCl2QAAAADkRJlZN2XUk1ZBoPkDAAAA5GyZ9o7fxcVFZcuW1ahRozR27NjMOiwAAAAA5BjUTQAAAACySqZ/5Cs+Pl5XrlzJ7MMCAAAAQI5B3QQAAADgabN5Cbi/P8zUZDLp6tWrWrt2rWrWrGl3YAAAAADg7KibAAAAADiKzQ2gvz/M1MXFRYULF1ZISIiGDBlid2AAAAAA4OyomwAAAAA4is0NoL8/zBQAAAAAYI26CQAAAICj2PwMoOHDh+vGjRspxk+fPq3IyEi7ggIAAACAnIC6CQAAAICj2NwA2rBhg+7du5di/NatW1qyZIldQQEAAABATkDdBAAAAMBRMrQE3Llz5zRy5EhJktls1qBBg+Tp6WnZbjQadfz4cfn7+2dulAAAAADgJKibAAAAAGQHGWoAlSxZUuXLl1diYqL27t0rX19f+fj4WLa7urqqTp066tChQ6YHCueRJ08eR4fg1Miffciffciffciffciffdzd3R0dgtOLjY3VG2+8keq28ePHq1WrVlZje/fu1dixY3XixAmVKlVKw4YNU7169bIgUmR31E1A2vh9D1tw3cAWXDewx549e9SlS5dUt1EbwJm4mM1msy07NmzYUP/5z3/k6+ub2TFZ/PDDDxo7dqy++eYbvfXWWxo6dKgCAgKs5gQFBWnXrl1Wn6izh9Fo1IEDB1StWjUZDIZMOSYAAMDTlpRslKuLeP9iI6PRqD179qhYsWJWOdyzZ4/Gjh2rqKgoFShQwDJ+/vx5tWjRQm+88YZatGihFStWaP369dq0aZNKlCjhgDOwD++Bn57MqpvOnj2r0qVLW17v27dPXl5eeuGFFxQXF6fff/9dTZo00YMHD7Ru3Tp17NhREyZM0LPPPqtu3bpZHevq1avq0KGDtm3bpjfffFPdunVTw4YN0x0L1wsAAM7PaDLL4Ori6DCynUfvcypVqqRr165ZbcsttQFS56zvgTN0B9BfRUVFZWYcqXrU1HF3d1fDhg0VHh6umTNnqnbt2qnOf/Dggc6ePatt27Zp7ty5lv3v3bunFStWqFq1aun+3kO/PKgjl1Ku1Q0AAJDdlC/qoykdq8toNDo6FKfm4eGh0qVLW72ZHzFihMLDw60KPElatmyZSpUqpcGDB0uSRo4cqaioKK1bt04RERFZGTayucyom86ePav27dsrIiJCnTp10oULF/Tee+9p9OjRunfvnnr37q2GDRuqcePGGjJkiBITE9WuXbsnHtfNzU2DBw/Wli1bMtQAeoSaCQAA5/SofkDavLy8rD58I1EbwDnZ3AC6efOmpk6dqgMHDuj27dsptn///fd2BSY9/B8tOTlZktS1a1cVLlxYZcqU0TfffKNx48ZJku7evWspVlq3bq0zZ86oUqVK6tGjh/r3769r164pLCwsxf+wT3L66j0dvpjyvAAAAJA77N69W7GxsZoxY0aKbdHR0VZLOri5uSk4OFgHDx7MyhDhBDKjbipdurQWL16st99+W/Hx8Vq/fr0+/PBD1a9fX/369VOdOnX07rvv6rPPPtP9+/c1c+ZMq2UhDxw4oPfee8/y2mg06urVq1ZNn23btmnKlCkpVlx4HGomAACQW1AbwFnZ3AAaOXKkYmJi1KxZMy1ZskS9e/fWqVOn9Msvv+j111/P0LHWrVun4cOHp7ndz8/P6nXXrl21a9cuSQ+XgIuKitL58+fVtWtXffnll1q5cqVl7tq1axUWFqaCBQtmKCYAAADkbvPnz1fLli1TfMJPerjMw9+Xc/D19dXRo0ezKDo4i8yqmypVqqQ1a9bI3d1d69atU1xcnIxGo1xcXHTlyhUlJiaqTJky2rZtmy5duqRSpUpZ9q1WrZoiIyN15swZSdKdO3c0c+ZM9evXz2pO2bJlM+/EAQAAchBqAzgrmxtA0dHRmjlzpmrVqqXt27erTZs2eu6557Ro0SKdOHEiw8erW7euFixYYDV2/vx5tW3bVnv27Hnsvvfu3VP//v01bNgwFSlSxDJuNBq1evVqTZkyJcPxAAAAOBuTyeToEJzWo+XzHv333Llz2rVrl9atW5fq0nr379+Xp6en1TZPT08lJCQ45VJ8zhizs8iMumnBggUqUKCA2rZtK0maO3euOnbsqCJFimjy5Mnq27evBgwYoFmzZunOnTt64403dPPmTUuD6Mcff1Tp0qVlNBpVs2bNFMffuHGjEhISaAABAJDL8B4wpb/XBVLuqw2QOmf9WdrcAPLy8pKb28Pdy5QpoxMnTui5555TaGiopkyZojFjxtgdnLe3t+7evSuz2SwXFxft3btX8fHxmjJlii5duiRJcnV1VWhoqAoVKqQxY8ZozJgxateunQwGg7Zu3aqCBQtmaBkDAAAAZ3XixAnFx8c7OgynFhMTI0lavny5ypUrp/j4eB04cCDFPDc3N506dcpq27lz5yQp1fnIvTKjbqpRo4YGDBigffv2adSoUSpRooSWLFmiEiVKyN3dXVOnTtWpU6ckSd26dVNgYKCCg4M1fvx4Pfvss+rWrZs+/PBDxcbG6ubNm0pMTNSDBw/03XffSfp/1y4AAMhdjh07Rv2Qhkd1gURtAOdmcwMoJCREO3bsUI0aNRQSEqLZs2fLzc1NGzZsUKFChTJ8vJ9++slqqbf27dvr3//+t1xcXHTz5k0VKlRIn332mV599VV9+eWXkqRTp05p0KBBioiIUOfOnS37Tps2TdLDAuvq1avau3evgoODbT1VAAAAp1ChQgW5uro6OgynZDQaFRMTo4CAABkMBg0ZMkQdOnRQtWrVUp1fvHhxubu7W21fsWKFKlSokOY+2dmj80fmy4y6qXr16lqzZo02bNggd3d3vfPOO9q9e3ea8+vWrZtq/fPyyy/r5Zdf1q1bt3T06FH1799f0sMlTQAAQO7z98duIGVdICnX1QZInbPWTDY3gN577z1LB7Nz58768ccf1a1bN+XJk0fjxo3L0LHi4+PVpEkTy1Jt06dP159//ilXV1cVK1ZMcXFx+v3333Xp0iW98cYbSkpK0pIlSzRt2jS5uLho8eLFWrx4sSRp06ZNluP6+/tr7NixGjp0qLZs2SIPDw9bTxcAACDbc3V1tRQpsI3BYNDx48cVFxenRo0apZnPoKAg7d69WwMGDJD0sBjYu3ev3n77bX4GsJJZdVOxYsXUo0cPSQ+f4TN16lSFhIRIelj3xMbGSpJ+/vlnS230d6tXr9aWLVuUnJysW7duafDgwZKkq1evKigoyNZTBAAATor3rWkzGAwyGAw6cuQItQGcms0NoH/84x/6xz/+IUnKkyePli5dqj///FPe3t6WJQ7S6/79+8qfP7/V67x580qSypcvrwMHDmjx4sUaOHCgPD099eabb+rXX39VmzZtNGrUKMt+1atXl9lstjp2nTp15Ovrq927d+vFF1+09XQBAACQS0RHR6tw4cJ6/vnnLWMmk0ndu3dXhw4dFBYWpvDwcLVr107Tp09XWFiYli9fLpPJpNatWzswcmRHmVU33bp1S23bttWWLVtsiiMgIECvvfaaatWqpXv37mnVqlXq2rWrpIdLk+TLl8+m4wIAAORk1AZwdjY3gCTp/Pnz2rx5sy5cuKCIiAg9++yz+vPPP+Xj45Oh7ubFixdVtGhRy+vbt29bHkAaFBSkiRMnqkGDBmrVqpUSExPVq1cvHT16VGfOnEnX8UuVKqX//e9/NIAAAADwRIcPH1blypWtxpKTk3X69GlduXJF0sM7LiIjIxUZGak5c+YoMDBQCxculI+PjyNCRjaXGXVTTEyMvLy85O7uLknq37+/5Wuj0ag6depIkpKSklS9evUU+7dr187ytbe3t6X5M3/+fFWqVInlSQAAAFJBbQBnZ3MDaOfOnerTp4/KlCmj06dP66233tKzzz6rDz74QKVKldKgQYPSfazjx4+rS5cuunPnjtzd3XX48GHVrl1bkpSYmKiEhAS98847lu+7cuVKNW3aVFu2bNFvv/1mOc6DBw+svr57964SExO1f/9+1a1bN0PnV7aItxKMGdoFAADAIcoXpbDITBMmTEgx5uHhoR07dliNNWnSRE2aNMmqsOCkMqtuiomJUdWqVS2vp02bZrUE3K5duySlvgTclClTtHHjRpnNZrm4uFhtu379ury8vOTt7a02bdqod+/e6T43aiYAAJwT9UP6URvA2dncAIqMjFSfPn3Uu3dvBQYGWsbbt2+vf//73+kuZG7duqXff/9dVatWVe3atZWYmKiAgAC9+OKLWr9+vZYuXaratWtr/fr1GjhwoOLi4ix3CzVp0iTFEnCPnDlzRjVr1pQk1apVSy+//HKGzm9826qs0QgAAJxGUrJRri5Pngcga2VW3bR37169+uqrNsUwYMAADRgwQN26ddMrr7yi119/3bJt2LBhqlWrltq0aZPh41IzAQDgvIwmswwUEECOZ3MD6H//+58aNmyYYrxw4cK6fPlyuo+zYsUKhYSEqGjRotq/f7+MRqM8PT01d+5czZo1S/Pnz5evr6/at2+vcuXK6ccff7Q0czZt2qTdu3dbjhUfH2/5ulKlShozZoxcXFxUoECBDJ+f0WikmLGB0WjUsWPH5OfnR/5sQP7sQ/7sQ/7sQ/7sQ/7sYzQadST2sKpUqeLoUAD8TWbUTYmJifrtt980YsQIy1ifPn0szxAyGo2Wu4GSk5MVFBSU6nE+/fRTvffee6patar8/f0lSTdu3JCHh0eGzukRaiZkFL/vYQuuG9iC6+bJaP4AuYPNDaCSJUvqt99+k5+fn9X4999/b/U8nyc5duyYIiIiHgbj5mYpYh48eKAvvvjCshb17Nmz9cEHHyg5OVnNmjXT999/r2bNmqV5B5CLi4sKFixo6+nBDn9txCHjyJ99yJ99yJ99yJ99yJ99kpKSHB0CgFRkRt10/fp1VatWTeXKlZMkGQwGzZo1S7Vq1UoxN7Ul4B4pVqyY/vOf/2j37t164YUX5OHhoaJFi1qaR0BW4Pc9bMF1A1tw3QCAHQ2g9957T++8847++OMPmc1mbdq0SefOndPGjRutPpn2JBMnTrQ0ff7qUVPokRdeeEHr16+3vG7durVatWplNWf//v2SpB49emTgTAAAAADg6ciMusnX19eqqTN37tw059auXdvyPNWhQ4emOic0NFSHDh2S2WzmU9EAAABADuZq644vvfSSli5dqtjYWPn4+Gjx4sU6e/asIiMj1blz53QfJ7XmT3q4uLjI1TX18D09PeXp6WnTcQEAAAAgs2RW3ZTZXF1daf4AAAAAOVyGui8dO3bUlClTVKxYMUlSYGCgQkJCNHHiRJueswMAAAAAOQ11EwAAAIDsIEN3AB04cECJiYlWY9OnT9fNmzczNSgAAAAAcFbUTQAAAACyA5uXgHvEbDZnRhwAAAAAkGNRNwEAAADIanY3gKSHz+MBAAAAAKSNugkAAABAVsrQM4AkaejQofLy8rK8Tk5O1vDhw5UnTx6reQsXLrQ/OgAAAABwQtRNAAAAABwtQw2g1q1bpxhr2bJlpgUDAAAAAM6OugkAAABAdpChBtDYsWOfVhwAAAAAkCNQNwEAAADIDjLlGUAAAAAAAAAAAADIPmgAAQAAAAAAAAAA5DA0gJDp/v5gW2QM+bMP+bMP+bMP+bOPu7u7o0PIMW7cuKFhw4YpJCREgYGB6tOnT6rz9u7dqzZt2iggIEDNmjXTzp07szhSAEBuw/sl2ILrBrbguoE99uzZIz8/v1T/bNiwIcV8aitkVw5vAJnNZs2ePVu3b9/O0H4//PCDPvnkkzS3161bV7t27dJ7772n+Pj4DMdlMBgyvA8e5s3f35/82Yj82Yf82Yf82Yf82cdgMKiyfxVHh5Ej3L17V506ddLVq1c1ffp0rVq1Ss2bN08x7/z58+rRo4dCQ0O1du1aBQcHq1+/foqLi3NA1ABs9cMPP6hJkyZKSkpS586dFRMTk2JOUFCQEhISMv178zsPGcX7JdiC6wa24Lp5MqPJ7OgQsrXAwEBt27bN6s8nn3wib29vNWjQwGoutRWyMzdHB+Di4qJTp05p8ODBmj17tlxcXNSwYUNduHAh1fl169bVggULdPfuXV25ciXN43p4eKhQoUKSpAEDBmju3LkZimvolwd15NK9DO0DAABsU76oj6Z0rC6j0ejoUJzevHnzZDKZNGvWLHl4eEiSKleunGLesmXLVKpUKQ0ePFiSNHLkSEVFRWndunWKiIjI0pgB2M7T01PSw7soGzZsqPDwcM2cOVO1a9dOdf6DBw909uxZbdu2TXPnzrXsf+/ePa1YsULVqlVL9/emZgIAwDk9qr+QNi8vL5UuXdpqbMSIEQoPD1eBAgWsxqmtkJ05vAEkSR999JFatmypGTNmqF+/ftqyZYvM5tS70K6uKW9aunPnTqrNoAsXLqhXr16KjY3VjRs3LA2h9Dh99Z4OX8zYXUkAAACOtm7dOvXq1cvS/ElLdHS06tWrZ3nt5uam4OBgHTx48GmHCCATeXl5KTk5WZLUtWtXFS5cWGXKlNE333yjcePGSXp4Z2D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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:21.691834Z",
     "start_time": "2024-09-27T13:00:11.282420Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将特征重要性排名前三的城市进行二值化：\n",
    "df_Master['is_zibo_UserInfo_2'] = df_Master.apply(lambda x:1 if x.UserInfo_2=='淄博' else 0,axis=1)\n",
    "df_Master['is_chengdu_UserInfo_2'] = df_Master.apply(lambda x:1 if x.UserInfo_2=='成都' else 0,axis=1)\n",
    "df_Master['is_yantai_UserInfo_2'] = df_Master.apply(lambda x:1 if x.UserInfo_2=='烟台' else 0,axis=1)\n",
    " \n",
    "df_Master['is_zibo_UserInfo_4'] = df_Master.apply(lambda x:1 if x.UserInfo_4=='淄博' else 0,axis=1)\n",
    "df_Master['is_qingdao_UserInfo_4'] = df_Master.apply(lambda x:1 if x.UserInfo_4=='青岛' else 0,axis=1)\n",
    "df_Master['is_shantou_UserInfo_4'] = df_Master.apply(lambda x:1 if x.UserInfo_4=='汕头' else 0,axis=1)\n",
    " \n",
    "df_Master['is_zibo_UserInfo_8'] = df_Master.apply(lambda x:1 if x.UserInfo_8=='淄博' else 0,axis=1)\n",
    "df_Master['is_chengdu_UserInfo_8'] = df_Master.apply(lambda x:1 if x.UserInfo_8=='成都' else 0,axis=1)\n",
    "df_Master['is_heze_UserInfo_8'] = df_Master.apply(lambda x:1 if x.UserInfo_8=='菏泽' else 0,axis=1)\n",
    " \n",
    "df_Master['is_ziboshi_UserInfo_20'] = df_Master.apply(lambda x:1 if x.UserInfo_20=='淄博市' else 0,axis=1)\n",
    "df_Master['is_chengdushi_UserInfo_20'] = df_Master.apply(lambda x:1 if x.UserInfo_20=='成都市' else 0,axis=1)\n",
    "df_Master['is_sanmenxiashi_UserInfo_20'] = df_Master.apply(lambda x:1 if x.UserInfo_20=='三门峡市' else 0,axis=1)\n",
    " \n",
    " \n",
    "#特征衍生-IP地址变更次数特征\n",
    "df_Master['UserInfo_20'] = [a[:-1] if a.find('市')!= -1 else i[:] for a in df_Master.UserInfo_20]\n",
    "city_df = df_Master[['UserInfo_2','UserInfo_4','UserInfo_8','UserInfo_20']]\n",
    " \n",
    " \n",
    "city_change_cnt =[]\n",
    "for i in range(city_df.shape[0]):\n",
    "    a = list(city_df.iloc[i])\n",
    "    city_count = len(set(a))\n",
    "    city_change_cnt.append(city_count)\n",
    "df_Master['city_count_cnt'] = city_change_cnt\n",
    " \n",
    " \n",
    "# 3)运营商种类少,直接将其转换成哑变量\n",
    "print(df_Master.UserInfo_9.value_counts())\n",
    "print(set(df_Master.UserInfo_9))"
   ],
   "id": "f3f5e873c3344617",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UserInfo_9\n",
      "中国移动     25700\n",
      "中国联通      7389\n",
      "不详        6962\n",
      "中国电信      3287\n",
      "中国移动      2895\n",
      "中国电信      2052\n",
      "中国联通      1416\n",
      "Name: count, dtype: int64\n",
      "{'中国联通', '中国移动 ', '中国联通 ', '中国电信 ', '中国电信', '不详', '中国移动'}\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:21.819932Z",
     "start_time": "2024-09-27T13:00:21.693834Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_Master['UserInfo_9'] = df_Master.UserInfo_9.replace({'中国联通 ':'china_unicom',\n",
    "                              '中国联通':'china_unicom',\n",
    "                              '中国移动':'china_mobile',\n",
    "                              '中国移动 ':'china_mobile',\n",
    "                              '中国电信':'china_telecom',\n",
    "                              '中国电信 ':'china_telecom',\n",
    "                              '不详':'operator_unknown'\n",
    "    \n",
    "})\n",
    " \n",
    " \n",
    "operator_dummy = pd.get_dummies(df_Master.UserInfo_9)\n",
    "df_Master = pd.concat([df_Master,operator_dummy],axis=1)\n",
    " \n",
    "# 删除原变量\n",
    "df_Master = df_Master.drop(['UserInfo_9'],axis=1)\n",
    "df_Master = df_Master.drop(['UserInfo_19','UserInfo_2','UserInfo_4','UserInfo_7','UserInfo_8','UserInfo_20'],axis=1)\n",
    " \n",
    "# 看看还剩下哪些类型变量要处理\n",
    "df_Master.dtypes.value_counts()\n",
    "df_Master.select_dtypes(include='object')"
   ],
   "id": "c85dcee31d90ffe1",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "      WeblogInfo_19 WeblogInfo_20 WeblogInfo_21 ListingInfo sample_status\n",
       "0                 I            I5             D    2014/3/5         train\n",
       "1                 I            I5             D   2014/2/26         train\n",
       "2                 I            I5             D   2014/2/28         train\n",
       "3                 I            I5             D   2014/2/25         train\n",
       "4                 I           NaN             D   2014/2/27         train\n",
       "...             ...           ...           ...         ...           ...\n",
       "49696             I            I4             D   26/2/2014          test\n",
       "49697             I            I5             D   27/2/2014          test\n",
       "49698             I            I5             D  16/11/2013          test\n",
       "49699             I            I5             D   21/2/2014          test\n",
       "49700             I            I5             D   28/2/2014          test\n",
       "\n",
       "[49701 rows x 5 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WeblogInfo_19</th>\n",
       "      <th>WeblogInfo_20</th>\n",
       "      <th>WeblogInfo_21</th>\n",
       "      <th>ListingInfo</th>\n",
       "      <th>sample_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/3/5</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/26</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/28</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/25</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>2014/2/27</td>\n",
       "      <td>train</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>49696</th>\n",
       "      <td>I</td>\n",
       "      <td>I4</td>\n",
       "      <td>D</td>\n",
       "      <td>26/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49697</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>27/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49698</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>16/11/2013</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49699</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>21/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49700</th>\n",
       "      <td>I</td>\n",
       "      <td>I5</td>\n",
       "      <td>D</td>\n",
       "      <td>28/2/2014</td>\n",
       "      <td>test</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49701 rows × 5 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:21.986461Z",
     "start_time": "2024-09-27T13:00:21.820936Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 可以看到,我们要将这些weibo变量进行处理\n",
    "# 4) 微博特征\n",
    "for col in ['WeblogInfo_19','WeblogInfo_20','WeblogInfo_21']:\n",
    "    df_Master[col].replace({'nan':np.nan})   # 将字符型的nan,利用众数填充\n",
    "    df_Master[col] = df_Master[col].fillna(df_Master[col].mode()[0])\n",
    " \n",
    "# 看看这些变量有几种类型的值\n",
    "for col in ['WeblogInfo_19','WeblogInfo_20','WeblogInfo_21']:\n",
    "    print(df_Master[col].value_counts())\n",
    "    print('\\n')\n",
    " \n",
    "# 这里我们猜测WeblogInfo_20是WeblogInfo_19和21的更细化表达,这里直接删除该变量\n",
    "# 对其他变量进行哑变量处理\n",
    " \n",
    "df_Master['WeblogInfo_19'] = ['WeblogInfo_19'+ i for i in df_Master.WeblogInfo_19]\n",
    "df_Master['WeblogInfo_21'] = ['WeblogInfo_21'+ i for i in df_Master.WeblogInfo_21]\n",
    " \n",
    "for col in ['WeblogInfo_19','WeblogInfo_21']:\n",
    "    weibo_dummy = pd.get_dummies(df_Master[col])\n",
    "    df_Master = pd.concat([df_Master,weibo_dummy],axis=1)\n",
    "    \n",
    "# 删除原变量\n",
    "df_Master = df_Master.drop(['WeblogInfo_19','WeblogInfo_21','WeblogInfo_20'],axis=1)\n",
    " \n",
    "# 至此,类别变量处理完毕\n",
    "df_Master.dtypes.value_counts()"
   ],
   "id": "6270192961d6bba9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WeblogInfo_19\n",
      "I    41193\n",
      "D     5900\n",
      "F     1980\n",
      "E      314\n",
      "J      174\n",
      "G      135\n",
      "H        5\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "WeblogInfo_20\n",
      "I5     31740\n",
      "I4      4122\n",
      "C21     4057\n",
      "U       2965\n",
      "I3      2756\n",
      "C19     1252\n",
      "C20      904\n",
      "C1       393\n",
      "C11      381\n",
      "F16      231\n",
      "C15      201\n",
      "C18      153\n",
      "C12       91\n",
      "C17       78\n",
      "F14       56\n",
      "F15       53\n",
      "F13       46\n",
      "I10       38\n",
      "I7        28\n",
      "C16       25\n",
      "O         21\n",
      "I6        20\n",
      "F11       19\n",
      "C13       18\n",
      "F12       13\n",
      "F10        9\n",
      "C38        7\n",
      "I8         6\n",
      "F9         5\n",
      "I11        3\n",
      "C39        2\n",
      "F3         2\n",
      "C14        1\n",
      "F7         1\n",
      "F6         1\n",
      "C32        1\n",
      "F5         1\n",
      "F2         1\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n",
      "WeblogInfo_21\n",
      "D    40871\n",
      "A     6152\n",
      "C     2596\n",
      "B       82\n",
      "Name: count, dtype: int64\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "float64    121\n",
       "int64       41\n",
       "bool        15\n",
       "object       2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:23.559896Z",
     "start_time": "2024-09-27T13:00:21.987464Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 我们来看看借款的成交时间趋势\n",
    "# 首先将字符型的日期转换成时间戳形式\n",
    "import datetime\n",
    "from datetime import datetime\n",
    "df_Master['ListingInfo'] = pd.to_datetime(df_Master.ListingInfo, errors='coerce', format='%Y/%m/%d')\n",
    "# 删除 'ListingInfo' 列中值为 NaT 的行\n",
    "df_Master = df_Master.dropna(subset=['ListingInfo'])\n",
    "# df_Master['ListingInfo']\n",
    "df_Master[\"Month\"] = df_Master.ListingInfo.apply(lambda x:datetime.strftime(x,\"%Y-%m\"))\n",
    "df_Master[\"Month\"]\n",
    "\n",
    "plt.figure(figsize=(20,4))\n",
    "plt.title(\"借款成功的时间趋势变化\")\n",
    "plt.rcParams['font.sans-serif']=['Microsoft YaHei']\n",
    "sns.countplot(data=df_Master.sort_values('Month'),x='Month')\n",
    "plt.show()"
   ],
   "id": "1b53a1ceef9ad6b7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x400 with 1 Axes>"
      ],
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:24.434760Z",
     "start_time": "2024-09-27T13:00:23.561894Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 也可以看看违约率的月变化趋势 \n",
    "month_group = df_Master.groupby('Month')\n",
    "df_badrate_month = pd.DataFrame()\n",
    "df_badrate_month['total'] = month_group.target.count()\n",
    "df_badrate_month['bad'] = month_group.target.sum()\n",
    "df_badrate_month['badrate'] = df_badrate_month['bad']/df_badrate_month['total']\n",
    "df_badrate_month=df_badrate_month.reset_index()\n",
    " \n",
    " \n",
    "plt.figure(figsize=(12,4))\n",
    "plt.title('违约率的时间趋势图')\n",
    "sns.pointplot(data=df_badrate_month,x='Month',y='badrate',linestyles='-')\n",
    "plt.show()\n",
    "# 注:空值的部分代表的是预测样本"
   ],
   "id": "a3a4fd5560c598b0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:24.578311Z",
     "start_time": "2024-09-27T13:00:24.438756Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 我们不对数值型变量的缺失值做处理\n",
    "df_Master = df_Master.drop('Month',axis=1)\n",
    " \n",
    "# LogInfo表\n",
    "df_LogInfo\n",
    " \n",
    "# 衍生的变量有\n",
    "# 1)累计登陆次数\n",
    "# 2)登陆时间的平均间隔\n",
    "# 3)最近一次的登陆时间距离成交时间差\n",
    " \n",
    "# 1)累计登陆次数\n",
    "log_cnt = df_LogInfo.groupby('Idx',as_index=False).LogInfo3.count().rename(\n",
    "    columns={'LogInfo3':'log_cnt'})\n",
    "log_cnt.head(10)\n",
    " "
   ],
   "id": "adfe42cba0630594",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Idx  log_cnt\n",
       "0    3       26\n",
       "1    4       11\n",
       "2    5       11\n",
       "3    8      125\n",
       "4   11       30\n",
       "5   12      199\n",
       "6   13       16\n",
       "7   16       15\n",
       "8   17       11\n",
       "9   18       34"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>log_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>13</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>17</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:00:25.619776Z",
     "start_time": "2024-09-27T13:00:24.580310Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 2)最近一次的登陆时间距离成交时间差\n",
    " \n",
    "# 最近一次的登录时间距离当前时间差\n",
    "df_LogInfo['Listinginfo1']=pd.to_datetime(df_LogInfo.Listinginfo1)\n",
    "df_LogInfo['LogInfo3'] = pd.to_datetime(df_LogInfo.LogInfo3)\n",
    "time_log_span = df_LogInfo.groupby('Idx',as_index=False).agg({'Listinginfo1':np.max,\n",
    "                                                       'LogInfo3':np.max})\n",
    "time_log_span.head()\n",
    " \n",
    "time_log_span['log_timespan'] = time_log_span['Listinginfo1']-time_log_span['LogInfo3']\n",
    "time_log_span['log_timespan'] = time_log_span['log_timespan'].map(lambda x:str(x))\n",
    " \n",
    "time_log_span['log_timespan'] = time_log_span['log_timespan'].map(lambda x:int(x[:x.find('d')]))\n",
    "time_log_span= time_log_span[['Idx','log_timespan']]\n",
    "time_log_span.head()"
   ],
   "id": "18d63f44ce13fff2",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_18712\\3412248051.py:6: FutureWarning: The provided callable <function max at 0x0000024AAFBBC900> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"max\" instead.\n",
      "  time_log_span = df_LogInfo.groupby('Idx',as_index=False).agg({'Listinginfo1':np.max,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   Idx  log_timespan\n",
       "0    3             4\n",
       "1    4             2\n",
       "2    5             1\n",
       "3    8             0\n",
       "4   11             0"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>log_timespan</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 28
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:16:27.316098Z",
     "start_time": "2024-09-27T13:16:19.276227Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 3)登陆时间的平均时间间隔\n",
    " \n",
    "df_temp_timeinterval = df_LogInfo.sort_values(by=['Idx','LogInfo3'],ascending=[True,True])\n",
    "\n",
    "# transform会确保返回的Series的索引与原始DataFrame的索引相匹配\n",
    "df_temp_timeinterval['LogInfo4']=df_temp_timeinterval.groupby('Idx')['LogInfo3'].transform(lambda x: x.shift(1))\n",
    "df_temp_timeinterval"
   ],
   "id": "1e877720a442bf5f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Idx Listinginfo1  LogInfo1  LogInfo2   LogInfo3   LogInfo4\n",
       "159876      3   2013-11-05         1         0 2013-08-30        NaT\n",
       "159877      3   2013-11-05         1         1 2013-08-30 2013-08-30\n",
       "159879      3   2013-11-05         1         4 2013-08-30 2013-08-30\n",
       "159880      3   2013-11-05         2         1 2013-08-30 2013-08-30\n",
       "159882      3   2013-11-05         4         1 2013-08-30 2013-08-30\n",
       "...       ...          ...       ...       ...        ...        ...\n",
       "965152  91704   2014-11-04         1        20 2014-10-26 2014-10-26\n",
       "965153  91704   2014-11-04         1         2 2014-10-26 2014-10-26\n",
       "965154  91704   2014-11-04         2         1 2014-10-26 2014-10-26\n",
       "965156  91704   2014-11-04         4         1 2014-10-26 2014-10-26\n",
       "965155  91704   2014-11-04         2        21 2014-10-30 2014-10-26\n",
       "\n",
       "[966431 rows x 6 columns]"
      ],
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       "      <th>Listinginfo1</th>\n",
       "      <th>LogInfo1</th>\n",
       "      <th>LogInfo2</th>\n",
       "      <th>LogInfo3</th>\n",
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       "  <tbody>\n",
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       "      <th>159876</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>1</td>\n",
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       "      <td>2013-08-30</td>\n",
       "      <td>NaT</td>\n",
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       "    <tr>\n",
       "      <th>159877</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>2013-08-30</td>\n",
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       "      <td>2013-08-30</td>\n",
       "      <td>2013-08-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159882</th>\n",
       "      <td>3</td>\n",
       "      <td>2013-11-05</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2013-08-30</td>\n",
       "      <td>2013-08-30</td>\n",
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       "      <th>965152</th>\n",
       "      <td>91704</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>2014-10-26</td>\n",
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       "      <th>965153</th>\n",
       "      <td>91704</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>2014-10-26</td>\n",
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       "    <tr>\n",
       "      <th>965154</th>\n",
       "      <td>91704</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965156</th>\n",
       "      <td>91704</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965155</th>\n",
       "      <td>91704</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2014-10-26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>966431 rows × 6 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 49
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:17:42.235620Z",
     "start_time": "2024-09-27T13:17:34.445331Z"
    }
   },
   "cell_type": "code",
   "source": [
    "df_temp_timeinterval['time_span'] = df_temp_timeinterval['LogInfo3'] - df_temp_timeinterval['LogInfo4']\n",
    "df_temp_timeinterval['time_span']  = df_temp_timeinterval['time_span'] .map(lambda x:str(x))\n",
    "df_temp_timeinterval['time_span'] = df_temp_timeinterval['time_span'].replace({'NaT':'0 days 00:00:00'})\n",
    "df_temp_timeinterval['time_span'] = df_temp_timeinterval['time_span'].map(lambda x:int(x[:x.find('d')]))\n",
    "df_temp_timeinterval"
   ],
   "id": "82c49f575bca5254",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "          Idx Listinginfo1  LogInfo1  LogInfo2   LogInfo3   LogInfo4  \\\n",
       "159876      3   2013-11-05         1         0 2013-08-30        NaT   \n",
       "159877      3   2013-11-05         1         1 2013-08-30 2013-08-30   \n",
       "159879      3   2013-11-05         1         4 2013-08-30 2013-08-30   \n",
       "159880      3   2013-11-05         2         1 2013-08-30 2013-08-30   \n",
       "159882      3   2013-11-05         4         1 2013-08-30 2013-08-30   \n",
       "...       ...          ...       ...       ...        ...        ...   \n",
       "965152  91704   2014-11-04         1        20 2014-10-26 2014-10-26   \n",
       "965153  91704   2014-11-04         1         2 2014-10-26 2014-10-26   \n",
       "965154  91704   2014-11-04         2         1 2014-10-26 2014-10-26   \n",
       "965156  91704   2014-11-04         4         1 2014-10-26 2014-10-26   \n",
       "965155  91704   2014-11-04         2        21 2014-10-30 2014-10-26   \n",
       "\n",
       "        time_span  \n",
       "159876          0  \n",
       "159877          0  \n",
       "159879          0  \n",
       "159880          0  \n",
       "159882          0  \n",
       "...           ...  \n",
       "965152          0  \n",
       "965153          0  \n",
       "965154          0  \n",
       "965156          0  \n",
       "965155          4  \n",
       "\n",
       "[966431 rows x 7 columns]"
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       "      <th>965154</th>\n",
       "      <td>91704</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965156</th>\n",
       "      <td>91704</td>\n",
       "      <td>2014-11-04</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
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       "      <td>2014-10-26</td>\n",
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       "      <th>965155</th>\n",
       "      <td>91704</td>\n",
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       "      <td>21</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2014-10-26</td>\n",
       "      <td>4</td>\n",
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       "<p>966431 rows × 7 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:17:51.061360Z",
     "start_time": "2024-09-27T13:17:50.976264Z"
    }
   },
   "cell_type": "code",
   "source": [
    "avg_log_timespan = df_temp_timeinterval.groupby('Idx',as_index=False).time_span.mean().rename(columns={'time_span':'avg_log_timespan'})\n",
    " \n",
    " \n",
    "log_info = pd.merge(log_cnt,time_log_span,how='left',on='Idx')\n",
    "log_info = pd.merge(log_info,avg_log_timespan,how='left',on='Idx')\n",
    "log_info.head()"
   ],
   "id": "c94423a4f4936a1a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Idx  log_cnt  log_timespan  avg_log_timespan\n",
       "0    3       26             4          2.423077\n",
       "1    4       11             2          0.636364\n",
       "2    5       11             1          1.181818\n",
       "3    8      125             0          0.096000\n",
       "4   11       30             0          0.266667"
      ],
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:22:16.825788Z",
     "start_time": "2024-09-27T13:19:14.902490Z"
    }
   },
   "cell_type": "code",
   "source": [
    "log_info.to_csv('./拍拍贷“魔镜杯”风控初赛数据/log_info_feature.csv',encoding='gbk',index=False)\n",
    " \n",
    "# 修改信息表\n",
    "# 衍生变量:\n",
    "# 1)最近的修改时间距离成交时间差;\n",
    "# 2)修改信息总次数\n",
    "# 3)每种信息修改的次数\n",
    "# 4)按照日期修改的次数\n",
    " \n",
    " \n",
    "# 1)最近的修改时间距离成交时间差;\n",
    "df_Userupdate['ListingInfo1']=pd.to_datetime(df_Userupdate['ListingInfo1'])\n",
    "df_Userupdate['UserupdateInfo2']=pd.to_datetime(df_Userupdate['UserupdateInfo2'])\n",
    "time_span = df_Userupdate.groupby('Idx',as_index=False).agg({'UserupdateInfo2':np.max,'ListingInfo1':np.max})\n",
    "time_span['update_timespan'] = time_span['ListingInfo1']-time_span['UserupdateInfo2']\n",
    "time_span['update_timespan'] = time_span['update_timespan'].map(lambda x:str(x))\n",
    "time_span['update_timespan'] = time_span['update_timespan'].map(lambda x:int(x[:x.find('d')]))\n",
    "time_span = time_span[['Idx','update_timespan']]\n",
    " \n",
    "# 2）计算每个用户修改不同类别信息的次数\n",
    "group = df_Userupdate.groupby(['Idx','UserupdateInfo1'],as_index=False).agg({'UserupdateInfo2':pd.Series.nunique})\n",
    " \n",
    "# 3）每种信息修改的次数的衍生\n",
    "user_df_list=[]\n",
    "for idx in group.Idx.unique():\n",
    "    user_df  = group[group.Idx==idx]\n",
    "    change_cate = list(user_df.UserupdateInfo1)\n",
    "    change_cnt = list(user_df.UserupdateInfo2)\n",
    "    user_col  = ['Idx']+change_cate\n",
    "    user_value = [user_df.iloc[0]['Idx']]+change_cnt\n",
    "    user_df2 = pd.DataFrame(np.array(user_value).reshape(1,len(user_value)),columns=user_col)\n",
    "    user_df_list.append(user_df2)\n",
    "cate_change_df = pd.concat(user_df_list,axis=0)\n",
    "cate_change_df.head()"
   ],
   "id": "5a0035357a5ce6c1",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_18712\\3861582976.py:14: FutureWarning: The provided callable <function max at 0x0000024AAFBBC900> is currently using SeriesGroupBy.max. In a future version of pandas, the provided callable will be used directly. To keep current behavior pass the string \"max\" instead.\n",
      "  time_span = df_Userupdate.groupby('Idx',as_index=False).agg({'UserupdateInfo2':np.max,'ListingInfo1':np.max})\n"
     ]
    },
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       "   Idx  _educationid  _hasbuycar  _idnumber  _lastupdatedate  \\\n",
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      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 53
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:22:26.373641Z",
     "start_time": "2024-09-27T13:22:16.827785Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将cate_change_df里的空值填为0\n",
    "cate_change_df = cate_change_df.fillna(0)\n",
    "cate_change_df.shape\n",
    " \n",
    "df_Userupdate\n",
    " \n",
    "# 4）修改信息的总次数，按照日期修改的次数的衍生\n",
    "update_cnt = df_Userupdate.groupby('Idx',as_index=False).agg({'UserupdateInfo2':pd.Series.nunique,\n",
    "                                                         'ListingInfo1':pd.Series.count}).\\\n",
    "                      rename(columns={'UserupdateInfo2':'update_time_cnt',\n",
    "                                      'ListingInfo1':'update_all_cnt'})\n",
    "update_cnt.head()"
   ],
   "id": "f7f9421cbcaf0741",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Idx  update_time_cnt  update_all_cnt\n",
       "0    3                1              13\n",
       "1    4                3              17\n",
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       "3    8                2              14\n",
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       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 54
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:24:23.220054Z",
     "start_time": "2024-09-27T13:24:23.102772Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将三个衍生特征的临时表进行关联\n",
    "update_info = pd.merge(time_span,cate_change_df,on='Idx',how='left')\n",
    "update_info = pd.merge(update_info,update_cnt,on='Idx',how='left')\n",
    "update_info.head()"
   ],
   "id": "805b6a896cccd9a0",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Idx  update_timespan  _educationid  _hasbuycar  _idnumber  _lastupdatedate  \\\n",
       "0    3               67           1.0         1.0        1.0              1.0   \n",
       "1    4                0           1.0         1.0        1.0              1.0   \n",
       "2    5               14           1.0         1.0        1.0              1.0   \n",
       "3    8                2           1.0         1.0        1.0              1.0   \n",
       "4   11                0           1.0         1.0        1.0              1.0   \n",
       "\n",
       "   _marriagestatusid  _mobilephone  _qq  _realname  _residencephone  \\\n",
       "0                1.0           1.0  1.0        1.0              1.0   \n",
       "1                1.0           3.0  1.0        1.0              1.0   \n",
       "2                1.0           1.0  1.0        1.0              1.0   \n",
       "3                1.0           2.0  1.0        1.0              1.0   \n",
       "4                1.0           3.0  1.0        2.0              1.0   \n",
       "\n",
       "   _residencetypeid  _residenceyears  _phone  _relationshipid  _age  _gender  \\\n",
       "0               1.0              1.0     0.0              0.0   0.0      0.0   \n",
       "1               1.0              1.0     1.0              1.0   0.0      0.0   \n",
       "2               1.0              1.0     0.0              0.0   0.0      0.0   \n",
       "3               1.0              1.0     0.0              0.0   0.0      0.0   \n",
       "4               1.0              1.0     1.0              1.0   0.0      0.0   \n",
       "\n",
       "   _regstepid  _byuserid  _flag_uctobcp  _flag_uctopvr  _companyaddress  \\\n",
       "0         0.0        0.0            0.0            0.0              0.0   \n",
       "1         0.0        0.0            0.0            0.0              0.0   \n",
       "2         0.0        0.0            0.0            0.0              0.0   \n",
       "3         0.0        0.0            0.0            0.0              0.0   \n",
       "4         0.0        0.0            0.0            0.0              0.0   \n",
       "\n",
       "   _department  _turnover  _companyphone  _companytypeid  _residenceaddress  \\\n",
       "0          0.0        0.0            0.0             0.0                0.0   \n",
       "1          0.0        0.0            0.0             0.0                0.0   \n",
       "2          0.0        0.0            0.0             0.0                0.0   \n",
       "3          0.0        0.0            0.0             0.0                0.0   \n",
       "4          0.0        0.0            0.0             0.0                0.0   \n",
       "\n",
       "   _companyname  _iscash  _workyears  _incomefrom  _companysizeid  _nickname  \\\n",
       "0           0.0      0.0         0.0          0.0             0.0        0.0   \n",
       "1           0.0      0.0         0.0          0.0             0.0        0.0   \n",
       "2           0.0      0.0         0.0          0.0             0.0        0.0   \n",
       "3           0.0      0.0         0.0          0.0             0.0        0.0   \n",
       "4           0.0      0.0         0.0          0.0             0.0        0.0   \n",
       "\n",
       "   _webshoptypeid  _webshopurl  _dormitoryphone  _schoolname  _graduatedate  \\\n",
       "0             0.0          0.0              0.0          0.0            0.0   \n",
       "1             0.0          0.0              0.0          0.0            0.0   \n",
       "2             0.0          0.0              0.0          0.0            0.0   \n",
       "3             0.0          0.0              0.0          0.0            0.0   \n",
       "4             0.0          0.0              0.0          0.0            0.0   \n",
       "\n",
       "   _graduateschool  _idaddress  _hasppdaiaccount  _phonetype  _ppdaiaccount  \\\n",
       "0              0.0         0.0               0.0         0.0            0.0   \n",
       "1              0.0         0.0               0.0         0.0            0.0   \n",
       "2              0.0         0.0               0.0         0.0            0.0   \n",
       "3              0.0         0.0               0.0         0.0            0.0   \n",
       "4              0.0         0.0               0.0         0.0            0.0   \n",
       "\n",
       "   _secondemail  _secondmobile  _otherwebshoptype  _contactid  _creationdate  \\\n",
       "0           0.0            0.0                0.0         0.0            0.0   \n",
       "1           0.0            0.0                0.0         0.0            0.0   \n",
       "2           0.0            0.0                0.0         0.0            0.0   \n",
       "3           0.0            0.0                0.0         0.0            0.0   \n",
       "4           0.0            0.0                0.0         0.0            0.0   \n",
       "\n",
       "   _orderid  _userid  _bussinessaddress  _hasbusinesslicense  _cityid  \\\n",
       "0       0.0      0.0                0.0                  0.0      0.0   \n",
       "1       0.0      0.0                0.0                  0.0      0.0   \n",
       "2       0.0      0.0                0.0                  0.0      0.0   \n",
       "3       0.0      0.0                0.0                  0.0      0.0   \n",
       "4       0.0      0.0                0.0                  0.0      0.0   \n",
       "\n",
       "   _districtid  _provinceid  _hassborgjj  _position  update_time_cnt  \\\n",
       "0          0.0          0.0          0.0        0.0                1   \n",
       "1          0.0          0.0          0.0        0.0                3   \n",
       "2          0.0          0.0          0.0        0.0                1   \n",
       "3          0.0          0.0          0.0        0.0                2   \n",
       "4          0.0          0.0          0.0        0.0                4   \n",
       "\n",
       "   update_all_cnt  \n",
       "0              13  \n",
       "1              17  \n",
       "2              13  \n",
       "3              14  \n",
       "4              17  "
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Idx</th>\n",
       "      <th>update_timespan</th>\n",
       "      <th>_educationid</th>\n",
       "      <th>_hasbuycar</th>\n",
       "      <th>_idnumber</th>\n",
       "      <th>_lastupdatedate</th>\n",
       "      <th>_marriagestatusid</th>\n",
       "      <th>_mobilephone</th>\n",
       "      <th>_qq</th>\n",
       "      <th>_realname</th>\n",
       "      <th>_residencephone</th>\n",
       "      <th>_residencetypeid</th>\n",
       "      <th>_residenceyears</th>\n",
       "      <th>_phone</th>\n",
       "      <th>_relationshipid</th>\n",
       "      <th>_age</th>\n",
       "      <th>_gender</th>\n",
       "      <th>_regstepid</th>\n",
       "      <th>_byuserid</th>\n",
       "      <th>_flag_uctobcp</th>\n",
       "      <th>_flag_uctopvr</th>\n",
       "      <th>_companyaddress</th>\n",
       "      <th>_department</th>\n",
       "      <th>_turnover</th>\n",
       "      <th>_companyphone</th>\n",
       "      <th>_companytypeid</th>\n",
       "      <th>_residenceaddress</th>\n",
       "      <th>_companyname</th>\n",
       "      <th>_iscash</th>\n",
       "      <th>_workyears</th>\n",
       "      <th>_incomefrom</th>\n",
       "      <th>_companysizeid</th>\n",
       "      <th>_nickname</th>\n",
       "      <th>_webshoptypeid</th>\n",
       "      <th>_webshopurl</th>\n",
       "      <th>_dormitoryphone</th>\n",
       "      <th>_schoolname</th>\n",
       "      <th>_graduatedate</th>\n",
       "      <th>_graduateschool</th>\n",
       "      <th>_idaddress</th>\n",
       "      <th>_hasppdaiaccount</th>\n",
       "      <th>_phonetype</th>\n",
       "      <th>_ppdaiaccount</th>\n",
       "      <th>_secondemail</th>\n",
       "      <th>_secondmobile</th>\n",
       "      <th>_otherwebshoptype</th>\n",
       "      <th>_contactid</th>\n",
       "      <th>_creationdate</th>\n",
       "      <th>_orderid</th>\n",
       "      <th>_userid</th>\n",
       "      <th>_bussinessaddress</th>\n",
       "      <th>_hasbusinesslicense</th>\n",
       "      <th>_cityid</th>\n",
       "      <th>_districtid</th>\n",
       "      <th>_provinceid</th>\n",
       "      <th>_hassborgjj</th>\n",
       "      <th>_position</th>\n",
       "      <th>update_time_cnt</th>\n",
       "      <th>update_all_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>67</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 55
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:25:06.200781Z",
     "start_time": "2024-09-27T13:25:01.608379Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 保存数据至本地\n",
    "update_info.to_csv(r'./拍拍贷“魔镜杯”风控初赛数据/update_feature.csv',encoding='gbk',index=False)\n",
    " \n",
    "df_Master.to_csv(r'./拍拍贷“魔镜杯”风控初赛数据/df_Master_tackled.csv',encoding='gbk',index=False)"
   ],
   "id": "d490abf1db1261c0",
   "outputs": [],
   "execution_count": 56
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:26:06.622679Z",
     "start_time": "2024-09-27T13:26:05.146132Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 合并三个表的数据\n",
    "df_Master_tackled= pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/df_Master_tackled.csv',encoding='gbk')\n",
    "df_LogInfo_tackled = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/log_info_feature.csv',encoding='gbk')\n",
    "df_Userupdate_tackled = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/update_feature.csv',encoding='gbk')\n",
    " \n",
    "df_final = pd.merge(df_Master_tackled,df_LogInfo_tackled,on='Idx',how='left')\n",
    "df_final = pd.merge(df_final,df_Userupdate_tackled,on='Idx',how='left')\n",
    "df_final.shape"
   ],
   "id": "8c79f8f5294ee92a",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29832, 240)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 59
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:26:17.867807Z",
     "start_time": "2024-09-27T13:26:17.460822Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#########################################特征筛选#######################################\n",
    "# 用lightGBM筛选特征,\n",
    "# 这里训练10个模型,并对10个模型输出的特征重要性取平均,最后对特征重要性的值进行归一化\n",
    "# 以上将训练集和测试集合并是为了处理特征,现在再将两者划分开,用于模型训练\n",
    "# 将三万训练集划分成训练集和测试集,没有目标标签的2万样本作为预测集\n",
    " \n",
    "from sklearn.model_selection import train_test_split\n",
    " \n",
    "X_train,X_test, y_train, y_test = train_test_split(df_final[df_final.sample_status=='train'].drop(['Idx','sample_status','target','ListingInfo'],axis=1),\n",
    "                                                   df_final[df_final.sample_status=='train']['target'],\n",
    "                                                   test_size=0.3, \n",
    "                                                   random_state=0)\n",
    " \n",
    "train_fea =  np.array(X_train)\n",
    "test_fea = np.array(X_test)\n",
    "evaluate_fea = np.array(df_final[df_final.sample_status=='test'].drop(['Idx','sample_status','target','ListingInfo'],axis=1))\n",
    " \n",
    "# # reshape(-1,1转成一列\n",
    "train_label = np.array(y_train).reshape(-1,1)\n",
    "test_label = np.array(y_test).reshape(-1,1)\n",
    "evaluate_label = np.array(df_final[df_final.sample_status=='test']['target']).reshape(-1,1)\n",
    " \n",
    " \n",
    "fea_names = list(X_train.columns)\n",
    "feature_importance_values = np.zeros(len(fea_names)) "
   ],
   "id": "f86db7265a32d1d3",
   "outputs": [],
   "execution_count": 60
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:35:41.188653Z",
     "start_time": "2024-09-27T13:34:54.430005Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练10个lightgbm，并对10个模型输出的feature_importances_取平均\n",
    " \n",
    "import lightgbm as lgb \n",
    "from lightgbm import plot_importance\n",
    " \n",
    " \n",
    "for i in np.arange(10):\n",
    "    model = lgb.LGBMClassifier(n_estimators=1000,\n",
    "                              learning_rate=0.05,\n",
    "                              n_jobs=-1,\n",
    "                              early_stopping_rounds=100,\n",
    "                              verbose=-1)\n",
    "    model.fit(train_fea,train_label,\n",
    "              eval_metric='auc',\n",
    "             eval_set = [(test_fea, test_label)]\n",
    "             # early_stopping_rounds=100,\n",
    "              )\n",
    "    feature_importance_values += model.feature_importances_/10\n",
    "    \n",
    "# 将feature_importance_values存成临时表\n",
    "fea_imp_df1 = pd.DataFrame({'feature':fea_names,\n",
    "                           'fea_importance':feature_importance_values})\n",
    "fea_imp_df1 = fea_imp_df1.sort_values('fea_importance',ascending=False).reset_index(drop=True)\n",
    "fea_imp_df1['norm_importance'] = fea_imp_df1['fea_importance']/fea_imp_df1['fea_importance'].sum() # 特征重要性value的归一化\n",
    "fea_imp_df1['cum_importance'] = np.cumsum(fea_imp_df1['norm_importance'])# 特征重要性value的累加值\n",
    " \n",
    "fea_imp_df1"
   ],
   "id": "e8694e4f111584ba",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:99: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n",
      "D:\\Anaconda\\Lib\\site-packages\\sklearn\\preprocessing\\_label.py:134: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, dtype=self.classes_.dtype, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "                        feature  fea_importance  norm_importance  \\\n",
       "0              avg_log_timespan           306.0         0.018280   \n",
       "1    ThirdParty_Info_Period2_15           241.8         0.014444   \n",
       "2                   UserInfo_18           232.0         0.013859   \n",
       "3     ThirdParty_Info_Period3_5           224.4         0.013405   \n",
       "4     ThirdParty_Info_Period2_5           223.0         0.013321   \n",
       "..                          ...             ...              ...   \n",
       "231                   _nickname             0.0         0.000000   \n",
       "232              _companysizeid             0.0         0.000000   \n",
       "233              WeblogInfo_19H             0.0         0.000000   \n",
       "234              WeblogInfo_21B             0.0         0.000000   \n",
       "235              _webshoptypeid             0.0         0.000000   \n",
       "\n",
       "     cum_importance  \n",
       "0          0.018280  \n",
       "1          0.032724  \n",
       "2          0.046583  \n",
       "3          0.059988  \n",
       "4          0.073309  \n",
       "..              ...  \n",
       "231        1.000000  \n",
       "232        1.000000  \n",
       "233        1.000000  \n",
       "234        1.000000  \n",
       "235        1.000000  \n",
       "\n",
       "[236 rows x 4 columns]"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>fea_importance</th>\n",
       "      <th>norm_importance</th>\n",
       "      <th>cum_importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>avg_log_timespan</td>\n",
       "      <td>306.0</td>\n",
       "      <td>0.018280</td>\n",
       "      <td>0.018280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ThirdParty_Info_Period2_15</td>\n",
       "      <td>241.8</td>\n",
       "      <td>0.014444</td>\n",
       "      <td>0.032724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UserInfo_18</td>\n",
       "      <td>232.0</td>\n",
       "      <td>0.013859</td>\n",
       "      <td>0.046583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ThirdParty_Info_Period3_5</td>\n",
       "      <td>224.4</td>\n",
       "      <td>0.013405</td>\n",
       "      <td>0.059988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ThirdParty_Info_Period2_5</td>\n",
       "      <td>223.0</td>\n",
       "      <td>0.013321</td>\n",
       "      <td>0.073309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>_nickname</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>_companysizeid</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>WeblogInfo_19H</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>WeblogInfo_21B</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>_webshoptypeid</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>236 rows × 4 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 64
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:35:45.149784Z",
     "start_time": "2024-09-27T13:35:41.191648Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 特征重要性可视化\n",
    "plt.figure(figsize=(16,16))\n",
    "plt.rcParams['font.sans-serif']=['Microsoft YaHei']\n",
    "plt.subplot(3,1,1)\n",
    "plt.title('特征重要性')\n",
    "sns.barplot(data=fea_imp_df1.iloc[:10,:],x='norm_importance',y='feature')\n",
    " \n",
    "plt.subplot(3,1,2)\n",
    "plt.title('特征重要性累加图')\n",
    "plt.xlabel('特征个数')\n",
    "plt.ylabel('cum_importance')\n",
    "plt.plot(list(range(1, len(fea_names)+1)),fea_imp_df1['cum_importance'], 'r-')\n",
    " \n",
    "plt.subplot(3,1,3)\n",
    "plt.title('各个特征的归一化得分')\n",
    "plt.xlabel('特征')\n",
    "plt.ylabel('norm_importance')\n",
    "plt.plot(fea_imp_df1.feature,fea_imp_df1['norm_importance'], 'b*-')\n",
    "plt.show()"
   ],
   "id": "2711e6a591fe7780",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1600 with 3 Axes>"
      ],
      "image/png": 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D638/cue+9fo8ecx5qJn2AwCAVBF8AwAAAABcW0KCGVzfHGKn9jPpeVzcnR8vd24pMPDGI2/e24bYdn9/7YqMVOV69eTp55dhpw4AAFJG8A0AAAAAyFquXJHOnUtbkP3333c+EtvXVypQQMqf33zky2c+bg61//3Im9ecZiS97HYlxMSYo70BAMA9R/ANAAAAALj37HYzqD59Wjpz5tY/Y2LSv3+bzQymk4LstPxk5DUAAG6L4BsAAAAAcOfi4qRTp6STJ28daJ87Z4bfaeXjc+vQ+t9tgYGSp+e9O08AAOBSCL4BAAAAACm7fFk6ccIMtZN+3vz8xAkz0E4rm00qWFAqXNh8FCni/PPm5/7+3LwRAADcMYJvAAAAAMiOYmOlyEjp+HHpr7/Mn0mPpFD78uW07cvbWypWTCpaNPUgu0gRc3S2F/8MBQAA9x7fOAAAAADA3RiGFB3tHGr/++eZM2nbV548ZqgdFJT6z3z5GJ0NAACyFIJvAAAAAHA1CQnmqOx/j9a++fmVK7ffj6+vVLKkVKKE+ShZUipe3Ayzk4Jtf/97fz4AAAAZjOAbAAAAALKiS5ekw4elQ4du/Dx0SDpyxAy9ExNvv4+CBW8E2jf/THrOSG0AAOCmCL4BAAAAwCrXrkkHD0r79pmPpHD70CHp779v/docOW6E2CmF28WLmyO6AQAAsiGCbwAAAAC4186fvxFu79174/nRo+Z83KkpWFAqU0YqW/bGz/vvl0qVkgoVkjw8Mu0UAAAAXAnBNwAAAABkBLvdnGP73+H23r3SP/+k/rr77pMqVZIqVJDKl78RcpcpI+XOnXn1AwAAuBGCbwAAAABID8MwR2pv2yZt334j3D5wQIqNTf11pUpJFSveeFSqZP4sUIB5tgEAADIYwTcAAAAApMYwzPm2N2yQNm6Utm41A+9Ll1LePmdOc+T2v8Pt8uUlP79MLR0AACA7I/gGAAAAgCSnT98IuZN+XriQfDtvb6lyZSksTAoJuRF0lywpeXpmdtUAAAD4F4JvAAAAANnTtWvS5s3S2rXSn3+aIfeJE8m3y5lTql5dqllTqlFDqlbNDLlz5Mj8mgEAAJAmBN8AAAAAsofTp82Qe+1a6Y8/pC1bpPh45208PKTgYKlWLfNRs6YUGkrIDQAA4GIIvgEAAAC4n8REadcu6fffzZB77Vrp2LHk2xUqJNWrJ9WpI9WubY7s9vfP9HIBAACQsQi+AQAAALi+uDhzBPdvv0lr1piB97/n5vbwMEdv16snPfig+ShVSrLZrKgYAAAA9xDBNwAAAADXc/WqOS93UtC9bp05Z/fNcuUyw+2HHjJ/1qol5c5tTb0AAADIVATfAAAAALK+6GhzFPeaNeZj0yYpIcF5m3z5pPr1zUeDBlJYmOTFP3kAAACyI74FAgAAAMh6Tp26EXL/9ps5X7dhOG8TFGQG3Elhd6VK5nQmAAAAyPYIvgEAAABY7+xZ6X//Mx+rV0uHDyffpnz5G0F3gwZSyZLMzw0AAIAUEXwDAAAAyHwXL0q//iqtWmWG3bt2Oa+32aSqVZ1HdBcqZE2tAAAAcDkE3wAAAADuvWvXpLVrzaB71Spzju7EROdtqlaVHnlEatTIvCFl3ryWlAoAAADXR/ANAAAAIOMlJJjhdlLQvXatFBvrvE25clJ4uBl2P/ywVKCAJaUCAADA/RB8AwAAAMgYx49LP/4orVghrVwpXbrkvL5oUTPkDg83HyVKWFMnAAAA3B7BNwAAAIA7c+2a9NtvN8LuvXud1993nzltSVLYXaECN6MEAABApiD4BgAAAJA2hiHt32+G3D/+KP3yi3T9+o31Hh5SnTrS449LTZpINWpInp6WlQsAAIDsi+AbAAAAQOouXTLn6E4Ku//6y3l9sWJmyP3441LjxuYobwAAAMBiBN8AAAAAbkhMlLZtM4PuFSukdevMG1Um8faWGjS4EXaHhDB9CQAAALIcgm8AAAAgu4uONkdzL19u/vz7b+f15cqZQXeTJlLDhlKuXNbUCQAAAKQRwTcAAACQ3RiGtGePGXQvXy798Ydkt99Y7+9v3owyaVT3/fdbVysAAABwBwi+AQAAgOzg+nVp9eobYfexY87rg4OlJ56QmjaV6tUzpzQBAAAAXBTBNwAAAOCuoqKkZcukxYuln3+Wrl69sS5nTqlRIzPsfuIJqXRp6+oEAAAAMhjBNwAAAOBOTp2Sli41w+7Vq52nMClWzAy5n3zSnMqEuboBAADgpgi+AQAAAFd36JD0zTfmY90653WhoVKLFuajalXJZrOmRgAAACATEXwDAAAArujgQWnBAmnhQmnHDud1depILVuaYXfZstbUBwAAAFiI4BsAAABwFUePqtDMmfL44w9p69Yb7Z6e5nzdLVpITz9tTmkCAAAAZGME3wAAAEBWFhkpffWVtGCBPDduVFBSu6en1Lix1Lq1GXYHBlpZJQAAAJClEHwDAAAAWc3p0+YUJgsWSGvXOpoNDw9drlFD/q++Ko9nn5Xy57ewSAAAACDrIvgGAAAAsoJz56RFi8yw+7ffJMMw22026aGHpNatldi8uQ6eOaOwsDBzxDcAAACAFBF8AwAAAFa5fFn65htp7lxp5UopMfHGujp1zGlMWrW6MWe33S6dOWNNrQAAAIALIfgGAAAAMlNcnPTjj2bY/e230rVrN9bVqGGG3c89J5UsaV2NAAAAgIsj+AYAAADutcRE6Y8/pHnzzBtVRkXdWFe+vPTii1KbNlLZstbVCAAAALgRgm8AAADgXtm1yxzZ/eWX0l9/3WgvXFh6/nkz8K5Rw5zHGwAAAECGIfgGAAAAMlJkpBl0z50r7dhxoz0gQGrZ0gy7w8O5OSUAAABwDxF8AwAAAHcrOlr6+msz7P7tN8kwzPYcOaSmTc2w+6mnJF9fa+sEAAAAsgmCbwAAAOBOXL8uLV8uzZkjff+9edPKJPXrm2H3s89K+fJZVyMAAACQTRF8AwAAAGllGNK6ddIXX0gLFkgXL95YV7myGXa/8IJUsqR1NQIAAAAg+AYAAABu66+/pNmzpVmzpIMHb7QHBUlt2piBd5Uq1tUHAAAAwAnBNwAAAJCSK1ekRYvM0d2rV99o9/MzpzBp1056+GHJw8OyEgEAAACkjOAbAAAASJKYKP3yixl2L1okxcTcWNeokRl2P/OM5O9vWYkAAAAAbo/gGwAAADhwwJzGZPZs6fjxG+1ly5phd9u2zNsNAAAAuBCCbwAAAGRPly5JX30lzZhh3rAySZ48UuvWZuBdt65ks1lXIwAAAIA7woSEyPYMw9CECRP00EMPKSwsTF27dlVUVFSK2y5evFiPP/64KleurIiICP3yyy+ZWywAALg7hiH99pvUvr1UpIj02mtm6O3hITVrJs2fL50+LU2ZIj34IKE3AAAA4KIIvpHtffbZZ5o9e7bee+89zZgxQ0ePHlWfPn2Sbffnn39qwIABeumll/T111+rYcOG6tq1q44dO5b5RQMAgPQ5eVIaPlwqX15q2NCcw/vqValiRWnkSOnECWn5cnOkt6+v1dUCAAAAuEtMdYJsLTExUdOnT9cbb7yh8PBwSVLfvn312muvKTIyUsWLF3dsu2vXLlWqVElt27aVJFWsWFELFizQ3r17VapUKSvKBwAAtxIXJ333nTmVyYoV5o0rJfPGlM8/L736qlSnDqO6AQAAADdE8I1s7cCBA4qOjlb9+vUdbbVq1ZKHh4e2b9/uFHw3atRI06ZN07p161SrVi1999138vLyUu3atdN9XLvdniH1WynpHNzhXIDshusXbm/XLtk+/1y2uXNlO3/e0Ww89JCMV16R8cwzZvgt3QjDXQTXL+C6uH4B18S1C2Q9ab0eCb6RrUVGRkqSU8Dt4+OjwMBAnTlzxmnbMmXKqFevXmrfvr1sNps8PDw0depUBQYGpvu4O3fuvLvCsxB3Ohcgu+H6hTvxvHxZ9/34o/J/+61y7dnjaI/Ln1//PPmk/nnqKcWWLGk2HjpkUZUZh+sXcF1cv4Br4toFXA/BN7K1mJgYeXh4yNvb26ndx8dHcXFxTm3r1q3TiBEj1LdvX9WoUUMrVqxQz5499dVXX+n+++9P13FDQ0Pl6el51/VbyW63a+fOnW5xLkB2w/ULt5GYKP36q2wzZ8q2aJFs169LkgwvL+mpp5TYvr08H39cBb28VNDiUjMK1y/gurh+AdfEtQtkPUnX5e0QfCNb8/b2VmJiohISEuTldeNyiI2Nle+/bmw1ZswYtWjRQq+88ookqUqVKtqzZ48mTpyo0aNHp+u4np6ebvMXpjudC5DdcP3CZUVGSjNnSp9/Lh09eqM9OFjq0EG2l16SChaUO/durl/AdXH9Aq6JaxdwPQTfyNYKFSokSTpz5oyCgoIkSXFxcYqOjnaa/kSS9u/fr1atWjm1BQcHa82aNZlTLAAA2VlsrLR0qXmjyp9+kgzDbM+dW3rhBfNGlTVrcqNKAAAAAJIIvpHNhYSEyMfHR2vXrtVzzz0nSdqwYYNsNpseeOABp20LFSqkQ/+aE/TAgQOO8BwAANwD+/ZJkyZJc+ZIUVE32h9+2Ay7n3lG8vOzrDwAAAAAWRPBN7I1Hx8fvfDCCxo/fryKFCkiPz8/DRs2TK1bt1bu3LnVoUMHtW7dWo899pheeuklffTRR7r//vtVtWpV/fzzz1qzZo2mTJli9WkAAOBeEhOl77+XPvnEHN2dpFgxqX176ZVXpDJlLCsPAAAAQNZH8I1sr1evXrp+/bp69uwpT09PRUREqHfv3kpISNCRI0d07tw5SdLLL78swzD02Wef6ezZsypVqpQ++ugjPfzww9aeAAAA7uLiRXPe7gkTpMOHzTabTXrqKalzZ+mxxyTm1gQAAACQBgTfyPa8vb01ZMgQDRkyJNm61atXO57bbDa1b99e7du3z7ziAADIDvbuNcPuL76QYmLMtrx5pQ4dpC5dpPvvt7Q8AAAAAK6H4BsAAACZLzFR+uEHadw46eefb7SHhEjdu0svvijlymVdfQAAAABcGsE3AAAAMs/16+aNKj/6yLxxpSR5eEgREdKbb0qNGpnTmwAAAADAXSD4BgAAwL13/rw0aZI5pcn/3z9DuXNLr70mde0qlS5tbX0AAAAA3ArBNwAAAO6dgweljz+WZs6Url0z20qUkHr2NOfwzp3byuoAAAAAuCmCbwAAAGQsw5DWrpVGj5aWLjWXJal6dentt6Vnn5Vy5LC2RgAAAABujeAbAAAAGcNul775xpy/+88/b7Q/8YQZeDdsyPzdAAAAADIFwTcAAADuzpUr0uefS2PHSkeOmG3e3tLLL0u9ekmVKllaHgAAAIDsh+AbAAAAd+b8eWn8ePOGldHRZltgoHmzyq5dpUKFrK0PAAAAQLZF8A0AAID0OX7cnM5k2rQbN6wsW9Yc3d2uneTnZ219AAAAALI9gm8AAACkzd690siR0ty5UkKC2Va9utSvn9SiheTpaW19AAAAAPD/CL4BAABwaxs2SCNGSEuW3GgLD5f69pUaN+aGlQAAAACyHIJvAAAAJGcY0qpV0vDh0urVN9pbtJD69JFq17auNgAAAAC4DYJvAAAA3GAY0g8/SO+9J61fb7Z5eUkvvST17i1VqmRtfQAAAACQBgTfAAAAkBITpW+/ld5/X9q82Wzz8ZFee016+22pRAlr6wMAAACAdCD4BgAAyM4SE6VFi8zAe8cOs83PT+rSRXrrLalwYWvrAwAAAIA7QPANAACQHSUkSAsWSMOGSXv3mm0BAdKbb0r/+Y+UP7+19QEAAADAXSD4BgAAyE7i46W5c83A+9Ahsy1vXqlHD6l7dykw0NLyAAAAACAjEHwDAABkB3Fx0syZ0ogR0rFjZltgoNSrl9Stm5Qnj5XVAQAAAECGIvgGAABwZ9evS9OnSyNHSpGRZlvBguYNK994Q/L3t7Y+AAAAALgHCL4BAADcUVLgPWKEdPKk2VakiNSnj/Taa+YNLAEAAADATRF8AwAAuJPYWDPwHj78RuAdFCT17St16CD5+FhbHwAAAABkAoJvAAAAdxAbK82YYQbeJ06YbcWKSf37m4F3zpzW1gcAAAAAmYjgGwAAwJXFxd0IvJPm8C5a9EbgzQhvAAAAANkQwTcAAIAriouTZs6Uhg2Tjh8324oWlfr1kzp2JPAGAAAAkK0RfAMAALiSuDjpiy/MwPuvv8y2IkXMObw7dSLwBgAAAAARfAMAALiG+PgbgfexY2Zb4cI3Am9fX0vLAwAAAICshOAbAAAgK4uPl2bNkt5//0bgXaiQGXi//jqBNwAAAACkgOAbAAAgK4qPl2bPNgPvo0fNtkKFpD59zMDbz8/a+gAAAAAgCyP4BgAAyEoSEm4E3keOmG0FC5qBd+fOBN4AAAAAkAYE3wAAAFlBQoI0d640dKh0+LDZVqDAjcA7Vy5r6wMAAAAAF0LwDQAAYKWEBGnePDPwPnTIbMufX+rdW+rShcAbAAAAAO4AwTcAAIAV7HZpwQLp3XelAwfMtvz5pXfeMQNvf39r6wMAAAAAF0bwDQAAkJkSE6XFi6XBg6U9e8y2fPnMwLtrVwJvAAAAAMgABN8AAACZwTCkb781A+/t2822vHmlt96SevSQAgIsLQ8AAAAA3AnBNwAAwL1kGNIPP0iDBkmbN5ttAQHSf/5jPvLmtbQ8AAAAAHBHBN8AAAD3gmFIq1ZJ//2v9OefZluuXFL37tLbb0uBgdbWBwAAAABujOAbAAAgo/36qznC+7ffzGVfX3P+7t69pQIFrK0NAAAAALIBgm8AAICMsnatGXivWmUu58wpvf661LevVKSItbUBAAAAQDbiYXUBwJ1YunSpOnTooCZNmigyMlKS9Msvv+js2bMWVwYAyJY2bpSaNpXq1TND7xw5pDfekA4dksaNI/QGAAAAgExG8A2XM336dA0dOlSlSpXSiRMnlJCQIEnatGmTPvzwQ4urAwBkK9u2SRERUq1a0ooVkqen1LGjdOCANHGiFBRkdYUAAAAAkC0RfMPlzJ8/X0OHDtV///tfeXjc6MKNGjXShg0bLKwMAJBt7NkjPfusVK2a9N13koeH9PLL0v790rRpUqlSVlcIAAAAANkac3zD5Zw7d06lS5dO1u7p6amLFy9aUBEAINs4elQaMkSaPVsyDMlmk55/Xho8WKpQwerqAAAAAAD/jxHfcDk1atTQwoULHcs2m012u12ff/65ypUrZ2FlAAC3deaM1K2bGW7PmmWG3i1aSDt2SPPmEXoDAAAAQBbDiG+4nAEDBqh9+/bavXu37Ha7hg8friNHjujcuXOaMmWK1eUBANxJdLT04YfmDSqvXTPbHn1UGjZMqlnT2toAAAAAAKki+IbLKVOmjFasWKG5c+eqUKFCSkhIUNOmTdWmTRsVKVLE6vIAAO4gJsYMuz/8UEqaRqt2bWnECKlRI2trAwAAAADcFsE3XE5sbKw8PT3VqVMnp/Zr164pPj5eOXLksKgyAIDLi42Vpk41R3SfPWu2Va5sLj/1lDmnNwAAAAAgy2OOb7icF154QfPnz0/W/sUXX+jNN9+0oCIAgMuz26UvvjDn6u7e3Qy9779fmjNH2rZNiogg9AYAAAAAF0LwDZdz6NAh1alTJ1l7gwYNtHnzZgsqAgC4LMOQFi+WQkOl9u2lv/6SihSRJk2S9u6VXnxR8vS0ukoAAAAAQDoRfMPl5M6dW6dOnUrWHhUVJbvdbkFFAACXYxjSzz9LtWpJzzxjhtz33SeNHCkdOiR17ix5e1tdJQAAAADgDhF8w+WEh4dr1KhRioyMdLT9888/+vjjj1McCQ4AgJM//5TCw6XHHpM2bZJy5ZIGDpSOHpV695b8/KyuEAAAAABwl7i5JVzOO++8o1deeUXNmjVT2bJl5enpqcOHDytfvnwaO3as1eUBALKqnTvNgPvbb81lb2/pjTek/v2lggWtrQ0AAAAAkKEIvuFyAgICtGDBAv3444/aunWrDMNQ69atFRERoZw5c1pdHgAgqzl8WBo8WJo3z5zixMPDnM978GCpRAmrqwMAAAAA3AME33BJnp6eatasmZo1a2Z1KQCArOrUKWnoUOmzz6SEBLOtVSvpvfekihWtrQ0AAAAAcE8RfMPlJCQkaNGiRdq4caOio6NlGIbT+hkzZlhUGQAgS7hwwbxJ5dix0vXrZtvjj0vDhkk1alhZGQAAAAAgkxB8w+UMGTJEixYtUlhYmIKCguTlRTcGAEiKjZUmTTJHeUdFmW0PPigNHy41bGhtbQAAAACATEViCJfz008/afDgwXr++eczZH+GYejTTz/V/PnzdeXKFdWrV09Dhw5VYGBgitvOnTtXs2fP1smTJ5U/f35Nnz5dZcqUyZBaAAB3IDFRWrDAvEnlsWNmW6VK0gcfSE89JdlslpYHAAAAAMh8BN9wOf7+/goNDc2w/X322WeaPXu2RowYobx582rgwIHq06ePpk2blmzbsWPHauHCherdu7dCQkJ0/Phx5cqVK8NqAQCk0//+J/XuLW3ebC4XKSK9+670yisSvxEEAAAAANkW/yKEy+nevbtmzJihDz74QDly5LirfSUmJmr69Ol64403FB4eLknq27evXnvtNUVGRqp48eKObQ8fPqxp06Zp5syZqlWrliSpXLlyd3V8AMAd2rlT6tNH+uEHczkgwAzA//Mfif+QBAAAAIBsj+AbLmfp0qU6cOCAGjZsqDJlyiQLv9Nzc8sDBw4oOjpa9evXd7TVqlVLHh4e2r59u1PwvXTpUlWqVMkRet8Nu91+1/uwWtI5uMO5ANmNS1+/kZGyDRki26xZshmGDC8vGa+/LmPAAKlgQXMbVzwvII1c+voFsjmuX8A1ce0CWU9ar0eCb7icwoULq3Dhwhmyr8jISElyCrh9fHwUGBioM2fOOG27bds2VahQQR988IGWLl2q3Llzq3Xr1nrllVdkS+f8sTt37rz74rMIdzoXILtxpevX8/JlFZ45UwXnz5dHbKwkKapxY53q2lWxxYtLp06ZDyCbcKXrF4Azrl/ANXHtAq6H4BsuZ8SIERm2r5iYGHl4eMjb29up3cfHR3FxcU5tf//9tw4dOqRnnnlGU6dO1ebNm/Xhhx8qb968atmyZbqOGxoaKk9Pz7uu30p2u107d+50i3MBshuXun5jY2WbNEm24cNli4qSJBn16yvxgw+Up3Zt5bG4PCCzudT1C8AJ1y/gmrh2gawn6bq8HYJvuKTY2FgdPXpUly9fTrauZs2aad6Pt7e3EhMTlZCQIK+bboIWGxsrX19fp23tdrvKli2rt956S5IZXm/ZskVLlixJd/Dt6enpNn9hutO5ANlNlr5+ExOl+fOlAQOkY8fMtuBg6YMPZHvySXmm8zdtAHeTpa9fALfE9Qu4Jq5dwPUQfMPl/PHHH3r77bd14cIFSZJhGE5TjezduzfN+ypUqJAk6cyZMwoKCpIkxcXFKTo62mn6E0kKDAxUyZIlndpKly6tlStX3slpAABSs2qVeaPKLVvM5SJFpPfek9q3l7z46gIAAAAAuD0PqwsA0mvEiBF68MEHtXTpUvn4+Oj777/XlClTVKVKFU2ePDld+woJCZGPj4/Wrl3raNuwYYNsNpseeOABp22rVaumbdu2ObUdPHhQpUqVutNTAQDcbMcOqWlTqXFjM/QOCJDef186eFDq2JHQGwAAAACQZgTfcDknTpzQa6+9pvLly6tkyZKy2+1q0KCBevTooXHjxqVrXz4+PnrhhRc0fvx4rVmzRps3b9awYcPUunVr5c6dWx06dNBPP/0kSWrTpo0iIyP1/vvva/fu3frss8+0evVqvfLKK/fiNAEg+4iMNEdzh4VJK1aYAfebb0qHD5tTneTKZXWFAAAAAAAXw9ApuJwiRYrozJkzqlixosqXL68tW7aoXLly8vb21tGjR9O9v169eun69evq2bOnPD09FRERod69eyshIUFHjhzRuXPnJEnFixfX1KlTNXz4cM2fP1/FihXTRx99lGxkOAAgjS5ckEaMkMaNk2JjzbbnnpOGDZPKlrW0NAAAAACAayP4hst56qmn9Pnnn+vhhx9W06ZN9dZbb2nDhg3avHmzqlSpku79eXt7a8iQIRoyZEiydatXr3ZarlWrlpYsWXKHlQMAJJkh96efmgF3VJTZ1rCh9OGHUq1a1tYGAAAAAHALTHUCl9OlSxd98cUXkqRGjRpp4MCBunr1qurXr68PP/zQ4uoAAKlKTJTmzpUqVpTeessMvUNCpGXLpNWrCb0BAAAAABmGEd9wORs3blTVqlXl7e0tSWrZsqVatmypqKgonTlzRoUKFbK4QgBAMqtWSe+8I23dai4XLSoNHSq1ayd5elpbGwAAAADA7TDiGy7n5Zdf1tmzZ5O1//3332rbtq0FFQEAUrVrl9SsmdS4sRl6BwSYU5wcPCi9+iqhNwAAAADgnmDEN1zC+fPn9fvvv0uSDMPQzz//rMDAQMf6hIQErV69Wn5+flaVCAC42enT0qBB0owZ5hQnXl7SG29I//2vVKCA1dUBAAAAANwcwTdcgo+Pj959913FxcXJZrPpo48+clpvs9lUpEgRDRo0yKIKAQCSpCtXpI8+Mm9UefWq2fbss9KIEVLZstbWBgAAAADINgi+4RL8/f219f/nha1YsaJWrFih4sWLW1wVAMDBbpc+/9wc0X3mjNlWp44Zgj/4oLW1AQAAAACyHYJvuJzJkycrX758VpcBAJAkw5BWrDBvXLl7t9l2//3SBx+YI71tNmvrAwAAAABkS9zcEi6na9eu2rNnj9VlAAC2bZMee8y8eeXu3dJ990kffyzt2SO1akXoDQAAAACwDME3XE61atX0119/WV0GAGRfJ05I7dtL1atLK1dK3t7SW29Jhw9LPXtKOXNaXSEAAAAAIJtjqhO4nCZNmmjy5MnaunWrQkND5efn57T+qaeesqgyAHBzly5JI0dKY8ZI16+bbc8/Lw0fLpUubW1tAAAAAADchOAbLuf999+XJEVGRurrr792Wmez2Qi+ASCjxcdLn30mDR4s/f232Va/vjR6tFSrlrW1AQAAAACQAoJvuJx9+/ZZXQIAZA+GIX33ndS7t7R/v9lWrpz04YfS008zhzcAAAAAIMsi+IbLio+P1/Hjx5WQkKD7779fOXLksLokAHAfmzeb83b/+qu5nD+/OeL79dclPm8BAAAAAFkcwTdcjt1u14QJEzRjxgzFxcXJMAzlypVLr7/+ujp16mR1eQDg2k6elPr3l2bNMpdz5pT+8x+pb18pTx5rawMAAAAAII0IvuFyPvnkE82dO1dvv/22atasKUnauHGjPvnkE/n5+emll16yuEIAcEFXr0qjRpnTmFy9ara9+KJ548oSJaytDQAAAACAdCL4hsv55ptv1K9fP7Vs2dLRVrFiReXKlUtTpkwh+AaA9EhMlObOlfr1M0d7S9KDD0pjxki1a1tbGwAAAAAAd4jgGy7nn3/+UXBwcLL24OBgnTp1yoKKAMBF/f679Pbb0qZN5nLJktLIkdJzz3HjSgAAAACAS/OwugAgvYoVK6atW7cma9+6dasKFChgQUUA4GKOHlXpvn3l+fDDZugdECCNGCHt2ye1bk3oDQAAAABweYz4hst54YUXNGrUKMXGxurBBx+Uh4eH1q5dq3Hjxum1116zujwAyLouXZKGD5fHxx8rMC5OhoeHbB06SEOHSoUKWV0dAAAAAAAZhuAbLqd9+/a6ePGixo4dq5EjR8owDOXMmVOvvfaaOnfubHV5AJD1JCRI06dL//2v9Pffskm6VKuWck2eLM9q1ayuDgAAAACADEfwDZfUo0cPvf766zp48KC8vb1VqlQp5cyZ0+qyACDr+flnqVcvadcuc7lCBdlHjtTBYsUUVqWKtbUBAAAAAHCPEHzDJcXFxen3339XZGSk/P39FRcXp9DQUKvLAoCsY98+88aVy5eby4GB0pAhUufOkoeHtG2bldUBAAAAAHBPEXzD5ezatUvdunXT2bNnlT9/fl27dk0xMTGqXbu2xowZo8DAQKtLBADr/POPGXBPmiTZ7ZKXl9StmznNSdLno91uaYkAAAAAANxrHlYXAKTX4MGDFRQUpF9++UVr1qzRpk2btGDBAp09e1bvvfee1eUBgDXi46Vx46SyZaUJE8xwOyJC2r1b+vjjG6E3AAAAAADZAME3XM7Bgwf19ttvq1ChQo62KlWqqH///vrtt98srAwALLJypRQWJvXsKV24IFWpIq1aJS1dKpUvb3FxAAAAAABkPoJvuJySJUvq+vXrydoDAgLk6+trQUUAYJEjR6QWLaRHH5X27JHy55emTJG2bJHCw62uDgAAAAAAyxB8w+X0799f48eP16lTpxxtly5d0vjx49W1a1cLKwOATBITIw0cKAUHS0uWSJ6eUo8e0oEDUqdO5jIAAAAAANkYN7eEy3nvvfcUFRWlxo0bq2DBgsqRI4fOnDkjSTp58qS++OILp+1//PFHK8oEgIxnGNKCBdI770gnTphtjRubc3sHB1tbGwAAAAAAWQjBN1zOE088YXUJAJD5tm41R3WvWWMulypl3rTy6aclm83S0gAAAAAAyGoIvuFyunXrZnUJAJB5zp83pzWZOtUc8e3nJ/XvL731luTjY3V1AAAAAABkSQTfcElRUVHas2ePLly4IMMwHO02m01PPvmkhZUBQAZJSJAmTZIGDZIuXDDbXnhB+vBDKSjI0tIAAAAAAMjqCL7hcpYsWaLBgwcrNjY22TqCbwBuYdUqc1qT3bvN5apVpU8+kerXt7YuAAAAAABcBME3XM64cePUtGlT9ezZUwULFpSHh4fVJQFAxjh2zJzCZPFiczlfPmnYMKljR8nT09LSAAAAAABwJQTfcDmXLl1Su3btVLhwYatLAYCMcfWq9MEH0qhR0vXrZsjdpYv07rvSffdZXR0AAAAAAC6HobJwOc2aNdPy5cutLgMA7p5hSF99JVWsKA0daobe4eHStm3S+PGE3gAAAAAA3CFGfMPlDBw4UG3atFFERIRCQkLk5eXcjYcOHWpRZQCQDnv3St26Sf/7n7lcsqT00UdSy5aSzWZtbQAAAAAAuDiCb7iccePGaffu3QoKClJkZKRsNwVENsIiAFnd5cvSe+9JY8dKCQmSj4/Ut6/Uu7fk62t1dQAAAAAAuAWCb7ic+fPnq1evXurUqZPVpQBA2hmGNH++efPK06fNtqeflj7+WCpd2traAAAAAABwMwTfcDmBgYGqV6+e1WUAQNrt2mVOa/Lrr+ZymTLmHN7NmllbFwAAAAAAboqbW8Ll9O7dW9OmTdOlS5esLgUAbu3SJalXLykszAy9fX3Nm1ju2kXoDQAAAADAPcSIb7ic0aNH659//tGDDz6oQoUKJbu55Y8//mhRZQDw/wxDmjtXeucd6cwZs61FC3Nak5Ilra0NAAAAAIBsgOAbLiciIsLqEgAgdTt2mNOarFljLpcrJ33yifT449bWBQAAAABANkLwDZfTrVs3q0sAgOQuXJAGD5Y+/VSy2yU/P2ngQHOqk5w5ra4OAAAAAIBsheAbWd7WrVtVrVo1x/KpU6duuX3RokXvdUkAcINhSPPmmQH3uXNm27PPSh99JJUoYW1tAAAAAABkUwTfyPI6d+6sH3/8UXnz5pUkhYeHy2azJdvOMAzZbDbt3bs3kysEkG0dOCB16SKtWmUuV6hgTmvy6KPW1gUAAAAAQDZH8I0sb/r06Y7QW5Lmzp1rXTEAIEnXr0sffCCNGCHFxUk+Pua0Ju+8I3l7W10dAAAAAADZHsE3srzKlSs7LdeoUSPNrx06dKi6du2qwMDAjC4LQHa1cqU5yvvgQXP58cfNeb3LlLG2LgAAAAAA4OBhdQHAvbRgwQJdvHjR6jIAuIOzZ6UXXzSnMTl4UCpSRFqwQPrhB0JvAAAAAACyGIJvuDXDMKwuAYCrS0yUJk825++eN0+y2aQ335T27pWee85cBgAAAAAAWQpTnQAAkJrt26XXX5fWrzeXq1eXpkyRHnjA2roAAAAAAMAtMeIbAIB/u3JFeustqUYNM/QOCJDGj5c2bCD0BgAAAADABTDiGwCAmy1ZYk5lcuKEudyqlTR2rFS0qJVVAQAAAACAdCD4hltr3ry5AgICrC4DgCs4cULq1k1autRcLl1a+vRTqWlTa+sCAAAAAADpRvANlxQVFaU9e/bowoULyW5g+dRTTzmeDxs2LLNLA+Bq7HZp0iSpXz9zihMvL6l3b2nAAMnPz+rqAAAAAADAHSD4hstZsmSJBg8erNjY2GTrbDabU/ANALe0Y4fUqdONm1fWrStNnSpVrmxtXQAAAAAA4K5wc0u4nHHjxqlp06b65ZdftGfPHu3bt8/x2Lt3b7r3ZxiGJkyYoIceekhhYWHq2rWroqKibvmaS5cuqXbt2mrfvv0dngUAS127JvXvf+PmlblzSxMnSr//TugNAAAAAIAbIPiGy7l06ZLatWunwoULy8Pj7rvwZ599ptmzZ+u9997TjBkzdPToUfXp0+eWr5kyZYouXLhw18cGYIFVq6TQUGnECCkhQWrZUtqzR3rjDSkDPlMAAAAAAID1+Bc+XE6zZs20fPnyDNlXYmKipk+frjfeeEPh4eGqXr26+vbtq99++02RkZEpvubQoUNatGiRHnrooQypAUAmOX9eat9eatxYOnxYKlZM+uYbadEi8zkAAAAAAHAbzPENlzNw4EC1adNGERERCgkJkZeXczceOnRomvd14MABRUdHq379+o62WrVqycPDQ9u3b1fx4sWdtk9MTNTAgQPVuXNn7du3T2fOnLmjc7Db7Xf0uqwk6Rzc4Vzg5gxDtnnzZHvrLdnOn5dhs8no0kXG0KHmFCfZsA9z/QKui+sXcF1cv4Br4toFsp60Xo8E33A548aN0+7duxUUFKTIyEjZbDbHupufp0XSqO6bA24fHx8FBgamGGpPnjxZsbGxeumllzRw4MA7PANp586dd/zarMa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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 65
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:36:42.032271Z",
     "start_time": "2024-09-27T13:36:42.010538Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 剔除特征重要性为0的变量\n",
    "zero_imp_col = list(fea_imp_df1[fea_imp_df1.fea_importance==0].feature)\n",
    "fea_imp_df11 = fea_imp_df1[~(fea_imp_df1.feature.isin(zero_imp_col))]\n",
    "print('特征重要性为0的变量个数为 ：{}'.format(len(zero_imp_col)))\n",
    "print(zero_imp_col)"
   ],
   "id": "970a49645d7c066b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征重要性为0的变量个数为 ：25\n",
      "['_hassborgjj', '_position', '_secondmobile', '_hasbusinesslicense', '_userid', '_orderid', '_creationdate', '_contactid', '_otherwebshoptype', 'SocialNetwork_17', '_secondemail', '_ppdaiaccount', '_phonetype', '_hasppdaiaccount', '_idaddress', '_graduateschool', '_graduatedate', '_schoolname', '_dormitoryphone', '_webshopurl', '_nickname', '_companysizeid', 'WeblogInfo_19H', 'WeblogInfo_21B', '_webshoptypeid']\n"
     ]
    }
   ],
   "execution_count": 66
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:36:51.432029Z",
     "start_time": "2024-09-27T13:36:51.427653Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 剔除特征重要性比较弱的变量\n",
    "low_imp_col = list(fea_imp_df11[fea_imp_df11.cum_importance>=0.99].feature)\n",
    "print('特征重要性比较弱的变量个数为：{}'.format(len(low_imp_col)))\n",
    "print(low_imp_col)"
   ],
   "id": "ff1106fab4681fcf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "特征重要性比较弱的变量个数为：46\n",
      "['_companyname', 'china_unicom', 'is_chengdushi_UserInfo_20', 'WeblogInfo_19D', 'is_ziboshi_UserInfo_20', 'operator_unknown', 'UserInfo_13', 'is_hunan_UserInfo_19', '_residenceaddress', 'WeblogInfo_19F', 'is_qingdao_UserInfo_4', '_companyaddress', 'WeblogInfo_19I', 'WeblogInfo_21D', '_department', '_flag_uctobcp', '_educationid', 'is_sichuan_UserInfo_7', 'is_hunan_UserInfo_7', '_residencephone', '_marriagestatusid', 'is_jilin_UserInfo_19', '_age', 'is_heze_UserInfo_8', 'WeblogInfo_21A', '_phone', 'SocialNetwork_12', 'WeblogInfo_21C', 'is_zibo_UserInfo_8', 'is_zibo_UserInfo_2', 'is_hainan_UserInfo_7', 'is_yantai_UserInfo_2', '_companytypeid', '_hasbuycar', '_flag_uctopvr', 'WeblogInfo_19E', 'WeblogInfo_19J', '_workyears', '_residenceyears', 'WeblogInfo_19G', '_relationshipid', '_gender', '_byuserid', '_incomefrom', '_residencetypeid', '_bussinessaddress']\n"
     ]
    }
   ],
   "execution_count": 67
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:37:18.073593Z",
     "start_time": "2024-09-27T13:37:11.549419Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 删除特征重要性为0和比较弱的特征\n",
    "drop_imp_col = zero_imp_col+low_imp_col\n",
    "mydf_final_fea_selected = df_final.drop(drop_imp_col,axis=1)\n",
    "mydf_final_fea_selected.shape\n",
    " \n",
    "# (49701, 160)\n",
    " \n",
    "mydf_final_fea_selected.to_csv(\n",
    "    r'./拍拍贷“魔镜杯”风控初赛数据/mydf_final_fea_selected.csv',encoding='gbk',index=False)\n",
    " "
   ],
   "id": "b0a67a0c8ea52201",
   "outputs": [],
   "execution_count": 69
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:37:47.065293Z",
     "start_time": "2024-09-27T13:37:42.701021Z"
    }
   },
   "cell_type": "code",
   "source": [
    "##############################################建模######################################\n",
    "# 筛选完特征后,再将该数据集切分成训练集和测试集,并通过调参提高精度,然后使用精度最高的模型预测2万个样本的标签\n",
    " \n",
    "# 导入数据.用于建模\n",
    "df = pd.read_csv('./拍拍贷“魔镜杯”风控初赛数据/mydf_final_fea_selected.csv',encoding='gbk')\n",
    " \n",
    " \n",
    "x_data =  df[df.sample_status=='train'].drop(['Idx','sample_status','target','ListingInfo'],axis=1)\n",
    "y_data =  df[df.sample_status=='train']['target']\n",
    " \n",
    "# 划分训练集和测试集\n",
    "x_train,x_test, y_train, y_test = train_test_split(x_data,\n",
    "                                                   y_data,\n",
    "                                                   test_size=0.2)\n",
    " \n",
    " \n",
    " \n",
    "# 训练模型\n",
    "lgb_sklearn = lgb.LGBMClassifier(random_state=0).fit(x_train,y_train)\n",
    " \n",
    "# # 预测测试集的样本\n",
    "lgb_sklearn_pre  = lgb_sklearn.predict_proba(x_test)\n",
    " \n",
    " \n",
    " ###计算roc和auc\n",
    "from sklearn.metrics import roc_curve, auc \n",
    "def acu_curve(y,prob):\n",
    "    #  y真实,\n",
    "    #  prob预测\n",
    "    fpr,tpr,threshold = roc_curve(y,prob) ###计算真阳性率(真正率)和假阳性率(假正率)\n",
    "    roc_auc = auc(fpr,tpr) ###计算auc的值\n",
    " \n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.figure(figsize=(12,10))\n",
    "    plt.plot(fpr, tpr, color='darkorange',\n",
    "             lw=lw, label='ROC curve (AUC = %0.3f)' % roc_auc) ###假正率为横坐标，真正率为纵坐标做曲线\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('AUC')\n",
    "    plt.legend(loc=\"lower right\")\n",
    " \n",
    "    plt.show()\n",
    " \n",
    "acu_curve(y_test,lgb_sklearn_pre[:,1])"
   ],
   "id": "ad523941b2e1ff2f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 71
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:38:31.486393Z",
     "start_time": "2024-09-27T13:38:22.560712Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 以上是sklearn版,下面是原生版本\n",
    "import time\n",
    "# 原生的lightgbm\n",
    "lgb_train = lgb.Dataset(x_train,y_train)\n",
    "lgb_test = lgb.Dataset(x_test,y_test,reference=lgb_train)\n",
    "lgb_origi_params = {'boosting_type':'gbdt',\n",
    "              'max_depth':-1,\n",
    "              'num_leaves':31,\n",
    "              'bagging_fraction':1.0,\n",
    "              'feature_fraction':1.0,\n",
    "              'learning_rate':0.1,\n",
    "              'metric': 'auc'}\n",
    "start = time.time()\n",
    "lgb_origi = lgb.train(train_set=lgb_train,\n",
    "                      num_boost_round=400,\n",
    "                      params=lgb_origi_params,\n",
    "                      valid_sets=lgb_test)\n",
    "end = time.time()\n",
    "print('运行时间为{}秒'.format(round(end-start,0)))"
   ],
   "id": "43ad8a857486bab9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "运行时间为9.0秒\n"
     ]
    }
   ],
   "execution_count": 73
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:38:35.683094Z",
     "start_time": "2024-09-27T13:38:35.203440Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 原生的lightgbm的AUC\n",
    "lgb_origi_pre = lgb_origi.predict(x_test)\n",
    "acu_curve(y_test,lgb_origi_pre)"
   ],
   "id": "6db17f225b0ffd61",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 74
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:41:07.622290Z",
     "start_time": "2024-09-27T13:40:52.174544Z"
    }
   },
   "cell_type": "code",
   "source": [
    "########################################lightgbm尝试调参#################################\n",
    " \n",
    "# 确定最大迭代次数，学习率设为0.1 \n",
    "base_parmas={'boosting_type':'gbdt', # 使用的算法,还有rf,dart,goss\n",
    "             'learning_rate':0.1,\n",
    "             'num_leaves':40,        # 一棵树上的叶子数,默认31\n",
    "             'max_depth':-1,         # 树的最大深度,0：无限制\n",
    "             'bagging_fraction':0.8,  # 每次迭代随机选取部分数据\n",
    "             'feature_fraction':0.8,  # 每次迭代随机选取部分特征\n",
    "             'lambda_l1':0,           # 正则化,\n",
    "             'lambda_l2':0,\n",
    "             'min_data_in_leaf':20,   # 一个叶子上数据的最小数量,默认20，处理过拟合，设置较大可以避免生成一个较深的树，\n",
    "             'min_sum_hessian_inleaf':0.001,  # 一个叶子上最小hessian和,，处理过拟合\n",
    "             'metric':'auc'}\n",
    " \n",
    " \n",
    "cv_result = lgb.cv(train_set=lgb_train,\n",
    "                   num_boost_round=200,      #  迭代次数,默认100\n",
    "                   # early_stopping_rounds=5,  #  没有提高，模型将停止训练\n",
    "                   nfold=5,\n",
    "                   stratified=True,\n",
    "                   shuffle=True,\n",
    "                   params=base_parmas,\n",
    "                   metrics='auc',\n",
    "                   seed=0)\n",
    "cv_result\n",
    "print('最大的迭代次数: {}'.format(len(cv_result['valid auc-mean'])))\n",
    "print('交叉验证的AUC: {}'.format(max(cv_result['valid auc-mean'])))\n",
    " \n",
    " \n",
    "# 输出\n",
    "# 最大的迭代次数: 28\n",
    "# 交叉验证的AUC: 0.7136171096752256"
   ],
   "id": "99b1529e43d6dd06",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "最大的迭代次数: 200\n",
      "交叉验证的AUC: 0.7209857190327328\n"
     ]
    }
   ],
   "execution_count": 78
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:41:56.893059Z",
     "start_time": "2024-09-27T13:41:22.234907Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# num_leaves ，步长设为5\n",
    " \n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    " \n",
    " \n",
    "param_find1 = {'num_leaves':range(10,50,5)}\n",
    "cv_fold = StratifiedKFold(n_splits=5,random_state=0,shuffle=True)\n",
    "start = time.time()\n",
    "grid_search1 = GridSearchCV(estimator=lgb.LGBMClassifier(learning_rate=0.1,\n",
    "                                                         n_estimators = 28,\n",
    "                                                         max_depth=-1,\n",
    "                                                         min_child_weight=0.001,\n",
    "                                                         min_child_samples=20,\n",
    "                                                         subsample=0.8,\n",
    "                                                         colsample_bytree=0.8,\n",
    "                                                         reg_lambda=0,\n",
    "                                                         reg_alpha=0),\n",
    "                             cv = cv_fold,\n",
    "                             n_jobs=-1,\n",
    "                             param_grid = param_find1,\n",
    "                             scoring='roc_auc')\n",
    "grid_search1.fit(x_train,y_train)\n",
    "end = time.time()\n",
    "print('运行时间为:{}'.format(round(end-start,0)))\n",
    " \n",
    " \n",
    "print(grid_search1.get_params)\n",
    "print('\\t')\n",
    "print(grid_search1.best_params_)\n",
    "print('\\t')\n",
    "print(grid_search1.best_score_)\n",
    "grid_search1.get_params"
   ],
   "id": "71770bcb15dd3b59",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "运行时间为:35.0\n",
      "<bound method BaseEstimator.get_params of GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=0, shuffle=True),\n",
      "             estimator=LGBMClassifier(colsample_bytree=0.8, n_estimators=28,\n",
      "                                      reg_alpha=0, reg_lambda=0,\n",
      "                                      subsample=0.8),\n",
      "             n_jobs=-1, param_grid={'num_leaves': range(10, 50, 5)},\n",
      "             scoring='roc_auc')>\n",
      "\t\n",
      "{'num_leaves': 15}\n",
      "\t\n",
      "0.7250357660320118\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<bound method BaseEstimator.get_params of GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=0, shuffle=True),\n",
       "             estimator=LGBMClassifier(colsample_bytree=0.8, n_estimators=28,\n",
       "                                      reg_alpha=0, reg_lambda=0,\n",
       "                                      subsample=0.8),\n",
       "             n_jobs=-1, param_grid={'num_leaves': range(10, 50, 5)},\n",
       "             scoring='roc_auc')>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 79
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:43:00.366747Z",
     "start_time": "2024-09-27T13:42:26.807613Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# num_leaves,步长为1\n",
    "param_find2 = {'num_leaves':range(40,50,1)}\n",
    "grid_search2 = GridSearchCV(estimator=lgb.LGBMClassifier(n_estimators=28,\n",
    "                                                         learning_rate=0.1,\n",
    "                                                         min_child_weight=0.001,\n",
    "                                                         min_child_samples=20,\n",
    "                                                         subsample=0.8,\n",
    "                                                         colsample_bytree=0.8,\n",
    "                                                         reg_lambda=0,\n",
    "                                                         reg_alpha=0\n",
    "                                                         ),\n",
    "                            cv=cv_fold,\n",
    "                            n_jobs=-1,\n",
    "                            scoring='roc_auc',\n",
    "                            param_grid = param_find2)\n",
    "grid_search2.fit(x_train,y_train)\n",
    "print(grid_search2.get_params)\n",
    "print('\\t')\n",
    "print(grid_search2.best_params_)\n",
    "print('\\t')\n",
    "print(grid_search2.best_score_)"
   ],
   "id": "f616fcedf6c8cd6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<bound method BaseEstimator.get_params of GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=0, shuffle=True),\n",
      "             estimator=LGBMClassifier(colsample_bytree=0.8, n_estimators=28,\n",
      "                                      reg_alpha=0, reg_lambda=0,\n",
      "                                      subsample=0.8),\n",
      "             n_jobs=-1, param_grid={'num_leaves': range(40, 50)},\n",
      "             scoring='roc_auc')>\n",
      "\t\n",
      "{'num_leaves': 42}\n",
      "\t\n",
      "0.7200858117601726\n"
     ]
    }
   ],
   "execution_count": 80
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:44:53.750709Z",
     "start_time": "2024-09-27T13:43:23.504910Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#  确定num_leaves 为41 ，下面进行min_child_samples 和 min_child_weight的调参，设定步长为5\n",
    "param_find3 = {'min_child_samples':range(15,35,5),\n",
    "               'min_child_weight':[x/1000 for x in range(1,4,1)]}\n",
    "grid_search3 = GridSearchCV(estimator=lgb.LGBMClassifier(estimator=28,\n",
    "                                                         learning_rate=0.1,\n",
    "                                                         num_leaves=41,\n",
    "                                                         subsample=0.8,\n",
    "                                                         colsample_bytree=0.8,\n",
    "                                                         reg_lambda=0,\n",
    "                                                         reg_alpha=0\n",
    "                                                         ),\n",
    "                            cv=cv_fold,\n",
    "                            scoring='roc_auc',\n",
    "                            param_grid = param_find3,\n",
    "                            n_jobs=-1)\n",
    "start = time.time()\n",
    "grid_search3.fit(x_train,y_train)\n",
    "end = time.time()\n",
    "print('运行时间:{} 秒'.format(round(end-start,0)))\n",
    " \n",
    "print(grid_search3.get_params)\n",
    "print('\\t')\n",
    "print(grid_search3.best_params_)\n",
    "print('\\t')\n",
    "print(grid_search3.best_score_)"
   ],
   "id": "3e93ec4d65f9e739",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "运行时间:90.0 秒\n",
      "<bound method BaseEstimator.get_params of GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=0, shuffle=True),\n",
      "             estimator=LGBMClassifier(colsample_bytree=0.8, estimator=28,\n",
      "                                      num_leaves=41, reg_alpha=0, reg_lambda=0,\n",
      "                                      subsample=0.8),\n",
      "             n_jobs=-1,\n",
      "             param_grid={'min_child_samples': range(15, 35, 5),\n",
      "                         'min_child_weight': [0.001, 0.002, 0.003]},\n",
      "             scoring='roc_auc')>\n",
      "\t\n",
      "{'min_child_samples': 15, 'min_child_weight': 0.001}\n",
      "\t\n",
      "0.7359548610331854\n"
     ]
    }
   ],
   "execution_count": 81
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-09-27T13:55:40.711434Z",
     "start_time": "2024-09-27T13:51:24.856531Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 确定min_child_weight为0.001，min_child_samples为20,下面对subsample和colsample_bytree进行调参\n",
    "param_find4 = {'subsample':[x/10 for x in range(5,11,1)],\n",
    "               'colsample_bytree':[x/10 for x in range(5,11,1)]}\n",
    "grid_search4 = GridSearchCV(estimator=lgb.LGBMClassifier(estimator=28,\n",
    "                                                         learning_rate=0.1,\n",
    "                                                         min_child_samples=20,\n",
    "                                                         min_child_weight=0.001,\n",
    "                                                         num_leaves=41,\n",
    "                                                         reg_lambda=0,\n",
    "                                                         reg_alpha=0\n",
    "                                                         ),\n",
    "                            cv=cv_fold,\n",
    "                            scoring='roc_auc',\n",
    "                            param_grid = param_find4,\n",
    "                            n_jobs=-1)\n",
    "start = time.time()\n",
    "grid_search4.fit(x_train,y_train)\n",
    "end = time.time()\n",
    "print('运行时间:{} 秒'.format(round(end-start,0)))\n",
    "print(grid_search4.get_params)\n",
    "print('\\t')\n",
    "print(grid_search4.best_params_)\n",
    "print('\\t')\n",
    "print(grid_search4.best_score_)"
   ],
   "id": "da0ca82df194448e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "运行时间:256.0 秒\n",
      "<bound method BaseEstimator.get_params of GridSearchCV(cv=StratifiedKFold(n_splits=5, random_state=0, shuffle=True),\n",
      "             estimator=LGBMClassifier(estimator=28, num_leaves=41, reg_alpha=0,\n",
      "                                      reg_lambda=0),\n",
      "             n_jobs=-1,\n",
      "             param_grid={'colsample_bytree': [0.5, 0.6, 0.7, 0.8, 0.9, 1.0],\n",
      "                         'subsample': [0.5, 0.6, 0.7, 0.8, 0.9, 1.0]},\n",
      "             scoring='roc_auc')>\n",
      "\t\n",
      "{'colsample_bytree': 0.8, 'subsample': 0.5}\n",
      "\t\n",
      "0.7345700942561886\n"
     ]
    }
   ],
   "execution_count": 82
  }
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